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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="optimal control model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 27235</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: optimal control model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27235</span> Controlled Chemotherapy Strategy Applied to HIV Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shohel%20Ahmed">Shohel Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Abdul%20Alim"> Md. Abdul Alim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaiya%20Rahman"> Sumaiya Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimal control can be helpful to test and compare different vaccination strategies of a certain disease. The mathematical model of HIV we consider here is a set of ordinary differential equations (ODEs) describing the interactions of CD4+T cells of the immune system with the human immunodeficiency virus (HIV). As an early treatment setting, we investigate an optimal chemotherapy strategy where control represents the percentage of effect the chemotherapy has on the system. The aim is to obtain a new optimal chemotherapeutic strategy where an isoperimetric constraint on the chemotherapy supply plays a crucial role. We outline the steps in formulating an optimal control problem, derive optimality conditions and demonstrate numerical results of an optimal control for the model. Numerical results illustrate how such a constraint alters the optimal vaccination schedule and its effect on cell-virus interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemotherapy%20of%20HIV" title="chemotherapy of HIV">chemotherapy of HIV</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control%20involving%20ODEs" title=" optimal control involving ODEs"> optimal control involving ODEs</a>, <a href="https://publications.waset.org/abstracts/search?q=optimality%20conditions" title=" optimality conditions"> optimality conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=Pontryagin%E2%80%99s%20maximum%20principle" title=" Pontryagin’s maximum principle"> Pontryagin’s maximum principle</a> </p> <a href="https://publications.waset.org/abstracts/65162/controlled-chemotherapy-strategy-applied-to-hiv-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65162.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27234</span> Model Predictive Control Using Thermal Inputs for Crystal Growth Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a>, <a href="https://publications.waset.org/abstracts/search?q=crystal%20growth" title=" crystal growth"> crystal growth</a> </p> <a href="https://publications.waset.org/abstracts/88644/model-predictive-control-using-thermal-inputs-for-crystal-growth-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27233</span> Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither" title=" random dither"> random dither</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/63970/stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27232</span> Synchronization of Chaotic T-System via Optimal Control as an Adaptive Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Kheiri">Hossein Kheiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Bashir%20Naderi"> Bashir Naderi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Reza%20Niknam"> Mohamad Reza Niknam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we study the optimal synchronization of chaotic T-system with complete uncertain parameter. Optimal control laws and parameter estimation rules are obtained by using Hamilton-Jacobi-Bellman (HJB) technique and Lyapunov stability theorem. The derived control laws are optimal adaptive control and make the states of drive and response systems asymptotically synchronized. Numerical simulation shows the effectiveness and feasibility of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20stability" title="Lyapunov stability">Lyapunov stability</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronization" title=" synchronization"> synchronization</a>, <a href="https://publications.waset.org/abstracts/search?q=chaos" title=" chaos"> chaos</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20control" title=" adaptive control"> adaptive control</a> </p> <a href="https://publications.waset.org/abstracts/8820/synchronization-of-chaotic-t-system-via-optimal-control-as-an-adaptive-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8820.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27231</span> Study on Optimal Control Strategy of PM2.5 in Wuhan, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuling%20Xie">Qiuling Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Zhu"> Shanliang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zongdi%20Sun"> Zongdi Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20degree" title="grey relational degree">grey relational degree</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20programming" title=" nonlinear programming"> nonlinear programming</a> </p> <a href="https://publications.waset.org/abstracts/54538/study-on-optimal-control-strategy-of-pm25-in-wuhan-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27230</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27229</span> Computational Simulations on Stability of Model Predictive Control for Linear Discrete-Time Stochastic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20simulations" title="computational simulations">computational simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a> </p> <a href="https://publications.waset.org/abstracts/35462/computational-simulations-on-stability-of-model-predictive-control-for-linear-discrete-time-stochastic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27228</span> An Optimal Control Model for the Dynamics of Visceral Leishmaniasis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20M.%20Elmojtaba">Ibrahim M. Elmojtaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Rayan%20M.%20Altayeb"> Rayan M. Altayeb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visceral leishmaniasis (VL) is a vector-borne disease caused by the protozoa parasite of the genus leishmania. The transmission of the parasite to humans and animals occurs via the bite of adult female sandflies previously infected by biting and sucking blood of an infectious humans or animals. In this paper we use a previously proposed model, and then applied two optimal controls, namely treatment and vaccination to that model to investigate optimal strategies for controlling the spread of the disease using treatment and vaccination as the system control variables. The possible impact of using combinations of the two controls, either one at a time or two at a time on the spread of the disease is also examined. Our results provide a framework for vaccination and treatment strategies to reduce susceptible and infection individuals of VL in five years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visceral%20leishmaniasis" title="visceral leishmaniasis">visceral leishmaniasis</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment" title=" treatment"> treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=vaccination" title=" vaccination"> vaccination</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a> </p> <a href="https://publications.waset.org/abstracts/39487/an-optimal-control-model-for-the-dynamics-of-visceral-leishmaniasis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39487.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27227</span> Optimal Control of DC Motor Using Linear Quadratic Regulator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meetty%20Tomy">Meetty Tomy</a>, <a href="https://publications.waset.org/abstracts/search?q=Arxhana%20G%20Thosar"> Arxhana G Thosar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper provides the implementation of optimal control for an armature-controlled DC motor. The selection of error weighted Matrix and control weighted matrix in order to implement optimal control theory for improving the dynamic behavior of DC motor is presented. The closed loop performance of Armature controlled DC motor with derived linear optimal controller is then evaluated for the transient operating condition (starting). The result obtained from MATLAB is compared with that of PID controller and simple closed loop response of the motor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20motor" title=" DC motor"> DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20index" title=" performance index"> performance index</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/45943/optimal-control-of-dc-motor-using-linear-quadratic-regulator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27226</span> A Stokes Optimal Control Model of Determining Cellular Interaction Forces during Gastrulation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanhao%20Gao">Yuanhao Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Ping%20%20Lin"> Ping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Kees%20Weijer"> Kees Weijer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An optimal control system model is proposed for the cell flow in the process of chick embryo gastrulation in this paper. The target is to determine the cellular interaction forces which are hard to measure. This paper will take an approach to investigate the forces with the idea of the inverse problem. By choosing the forces as the control variable and regarding the cell flow as Stokes fluid, an objective functional will be established to match the numerical result of cell velocity with the experimental data. So that the forces could be determined by minimizing the objective functional. The Lagrange multiplier method is utilized to derive the state and adjoint equations consisting the optimal control system, which specifies the first-order necessary conditions. Finite element method is used to discretize and approximate equations. A conjugate gradient algorithm is given for solving the minimum solution of the system and determine the forces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model" title="optimal control model">optimal control model</a>, <a href="https://publications.waset.org/abstracts/search?q=Stokes%20equation" title=" Stokes equation"> Stokes equation</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=chick%20embryo%20gastrulation" title=" chick embryo gastrulation"> chick embryo gastrulation</a> </p> <a href="https://publications.waset.org/abstracts/52434/a-stokes-optimal-control-model-of-determining-cellular-interaction-forces-during-gastrulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">259</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27225</span> Optimal Driving Strategies for a Hybrid Street Type Motorcycle: Modelling and Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jhon%20Vargas">Jhon Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=Gilberto%20Osorio-Gomez"> Gilberto Osorio-Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20Manrique"> Tatiana Manrique</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents an optimal driving strategy proposal for a 125 c.c. street-type hybrid electric motorcycle with a parallel configuration. The results presented in this article are complementary regarding the control proposal of a hybrid motorcycle. In order to carry out such developments, a representative dynamic model of the motorcycle is used, in which also are described different optimization functionalities for predetermined driving modes. The purpose is to implement an off-line optimal driving strategy which distributes energy to both engines by minimizing an objective torque requirement function. An optimal dynamic contribution is found from the optimization routine, and the optimal percentage contribution for vehicle cruise speed is implemented in the proposed online PID controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20model" title="dynamic model">dynamic model</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20strategies" title=" driving strategies"> driving strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20hybrid%20motorcycle" title=" parallel hybrid motorcycle"> parallel hybrid motorcycle</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/133692/optimal-driving-strategies-for-a-hybrid-street-type-motorcycle-modelling-and-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27224</span> Optimal Bayesian Chart for Controlling Expected Number of Defects in Production Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Makis">V. Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Jafari"> L. Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20u-chart" title="Bayesian u-chart">Bayesian u-chart</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20design" title=" economic design"> economic design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20stopping" title=" optimal stopping"> optimal stopping</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a> </p> <a href="https://publications.waset.org/abstracts/62841/optimal-bayesian-chart-for-controlling-expected-number-of-defects-in-production-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">573</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27223</span> Modeling and Optimal Control of Pneumonia Disease with Cost Effective Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Getachew%20Tilahun">Getachew Tilahun</a>, <a href="https://publications.waset.org/abstracts/search?q=Oluwole%20Makinde"> Oluwole Makinde</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Malonza"> David Malonza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20effectiveness%20analysis" title="cost effectiveness analysis">cost effectiveness analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=pneumonia%20dynamics" title=" pneumonia dynamics"> pneumonia dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20analysis" title=" stability analysis"> stability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a> </p> <a href="https://publications.waset.org/abstracts/61514/modeling-and-optimal-control-of-pneumonia-disease-with-cost-effective-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27222</span> Toward a Characteristic Optimal Power Flow Model for Temporal Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zongjie%20Wang">Zongjie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhizhong%20Guo"> Zhizhong Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While the regular optimal power flow model focuses on a single time scan, the optimization of power systems is typically intended for a time duration with respect to a desired objective function. In this paper, a temporal optimal power flow model for a time period is proposed. To reduce the computation burden needed for calculating temporal optimal power flow, a characteristic optimal power flow model is proposed, which employs different characteristic load patterns to represent the objective function and security constraints. A numerical method based on the interior point method is also proposed for solving the characteristic optimal power flow model. Both the temporal optimal power flow model and characteristic optimal power flow model can improve the systems’ desired objective function for the entire time period. Numerical studies are conducted on the IEEE 14 and 118-bus test systems to demonstrate the effectiveness of the proposed characteristic optimal power flow model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20power%20flow" title="optimal power flow">optimal power flow</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20period" title=" time period"> time period</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=economy" title=" economy"> economy</a> </p> <a href="https://publications.waset.org/abstracts/61552/toward-a-characteristic-optimal-power-flow-model-for-temporal-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27221</span> Optimal Closed-loop Input Shaping Control Scheme for a 3D Gantry Crane</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Maghsoudi">Mohammad Javad Maghsoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Mohamed"> Z. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Husain"> A. R. Husain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Input shaping has been utilized for vibration reduction of many oscillatory systems. This paper presents an optimal closed-loop input shaping scheme for control of a three dimensional (3D) gantry crane system including. This includes a PID controller and Zero Vibration shaper which consider two control objectives concurrently. The control objectives are minimum sway of a payload and fast and accurate positioning of a trolley. A complete mathematical model of a lab-scaled 3D gantry crane is simulated in Simulink. Moreover, by utilizing PSO algorithm and a proposed scheme the controller is designed to cater both control objectives concurrently. Simulation studies on a 3D gantry crane show that the proposed optimal controller has an acceptable performance. The controller provides good position response with satisfactory payload sway in both rail and trolley responses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20gantry%20crane" title="3D gantry crane">3D gantry crane</a>, <a href="https://publications.waset.org/abstracts/search?q=input%20shaping" title=" input shaping"> input shaping</a>, <a href="https://publications.waset.org/abstracts/search?q=closed-loop%20control" title=" closed-loop control"> closed-loop control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20scheme" title=" optimal scheme"> optimal scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a> </p> <a href="https://publications.waset.org/abstracts/17219/optimal-closed-loop-input-shaping-control-scheme-for-a-3d-gantry-crane" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17219.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27220</span> Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taiki%20Baba">Taiki Baba</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20quantization" title=" random dither quantization"> random dither quantization</a> </p> <a href="https://publications.waset.org/abstracts/78538/numerical-simulations-on-feasibility-of-stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27219</span> Model Predictive Control with Unscented Kalman Filter for Nonlinear Implicit Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/97739/model-predictive-control-with-unscented-kalman-filter-for-nonlinear-implicit-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27218</span> Optimal Tracking Control of a Hydroelectric Power Plant Incorporating Neural Forecasting for Uncertain Input Disturbances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marlene%20Perez%20Villalpando">Marlene Perez Villalpando</a>, <a href="https://publications.waset.org/abstracts/search?q=Kelly%20Joel%20Gurubel%20Tun"> Kelly Joel Gurubel Tun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydropower" title="hydropower">hydropower</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20order%20neural%20network" title=" high order neural network"> high order neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a> </p> <a href="https://publications.waset.org/abstracts/132201/optimal-tracking-control-of-a-hydroelectric-power-plant-incorporating-neural-forecasting-for-uncertain-input-disturbances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132201.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27217</span> Optimal Hybrid Linear and Nonlinear Control for a Quadcopter Drone</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xinhuang%20Wu">Xinhuang Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=many-objective%20optimization" title=" many-objective optimization"> many-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20control" title=" linear control"> linear control</a>, <a href="https://publications.waset.org/abstracts/search?q=cascade%20controllers" title=" cascade controllers"> cascade controllers</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a>, <a href="https://publications.waset.org/abstracts/search?q=drones" title=" drones"> drones</a> </p> <a href="https://publications.waset.org/abstracts/164515/optimal-hybrid-linear-and-nonlinear-control-for-a-quadcopter-drone" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164515.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27216</span> An Optimal Control Model to Determine Body Forces of Stokes Flow</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanhao%20Gao">Yuanhao Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Pin%20Lin"> Pin Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Kees%20Weijer"> Kees Weijer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we will determine the external body force distribution with analysis of stokes fluid motion using mathematical modelling and numerical approaching. The body force distribution is regarded as the unknown variable and could be determined by the idea of optimal control theory. The Stokes flow motion and its velocity are generated by given forces in a unit square domain. A regularized objective functional is built to match the numerical result of flow velocity with the generated velocity data. So that the force distribution could be determined by minimizing the value of objective functional, which is also the difference between the numerical and experimental velocity. Then after utilizing the Lagrange multiplier method, some partial differential equations are formulated consisting the optimal control system to solve. Finite element method and conjugate gradient method are used to discretize equations and deduce the iterative expression of target body force to compute the velocity numerically and body force distribution. Programming environment FreeFEM++ supports the implementation of this model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model" title="optimal control model">optimal control model</a>, <a href="https://publications.waset.org/abstracts/search?q=Stokes%20equation" title=" Stokes equation"> Stokes equation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a> </p> <a href="https://publications.waset.org/abstracts/54716/an-optimal-control-model-to-determine-body-forces-of-stokes-flow" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27215</span> Solutions to Probabilistic Constrained Optimal Control Problems Using Concentration Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, optimal control problems subject to probabilistic constraints have attracted much attention in many research field. Although probabilistic constraints are generally intractable in optimization problems, several methods haven been proposed to deal with probabilistic constraints. In most methods, probabilistic constraints are transformed to deterministic constraints that are tractable in optimization problems. This paper examines a method for transforming probabilistic constraints into deterministic constraints for a class of probabilistic constrained optimal control problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a> </p> <a href="https://publications.waset.org/abstracts/57973/solutions-to-probabilistic-constrained-optimal-control-problems-using-concentration-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27214</span> An Inverse Optimal Control Approach for the Nonlinear System Design Using ANN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20P.%20Nanda%20Kumar">M. P. Nanda Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Dheeraj"> K. Dheeraj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The design of a feedback controller, so as to minimize a given performance criterion, for a general non-linear dynamical system is difficult; if not impossible. But for a large class of non-linear dynamical systems, the open loop control that minimizes a performance criterion can be obtained using calculus of variations and Pontryagin’s minimum principle. In this paper, the open loop optimal trajectories, that minimizes a given performance measure, is used to train the neural network whose inputs are state variables of non-linear dynamical systems and the open loop optimal control as the desired output. This trained neural network is used as the feedback controller. In other words, attempts are made here to solve the “inverse optimal control problem” by using the state and control trajectories that are optimal in an open loop sense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inverse%20optimal%20control" title="inverse optimal control">inverse optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=controller%20design" title=" controller design"> controller design</a> </p> <a href="https://publications.waset.org/abstracts/9888/an-inverse-optimal-control-approach-for-the-nonlinear-system-design-using-ann" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">553</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27213</span> Developing New Algorithm and Its Application on Optimal Control of Pumps in Water Distribution Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Rajabpour">R. Rajabpour</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Talebbeydokhti"> N. Talebbeydokhti</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Ahmadi"> M. H. Ahmadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=G-JPSO" title="G-JPSO">G-JPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=operation" title=" operation"> operation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pumping%20station" title=" pumping station"> pumping station</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20distribution%20networks" title=" water distribution networks"> water distribution networks</a> </p> <a href="https://publications.waset.org/abstracts/36259/developing-new-algorithm-and-its-application-on-optimal-control-of-pumps-in-water-distribution-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36259.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27212</span> Comparative Study of Deep Reinforcement Learning Algorithm Against Evolutionary Algorithms for Finding the Optimal Values in a Simulated Environment Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20optimization" title=" real-time optimization"> real-time optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid-MPO" title=" hybrid-MPO"> hybrid-MPO</a> </p> <a href="https://publications.waset.org/abstracts/159906/comparative-study-of-deep-reinforcement-learning-algorithm-against-evolutionary-algorithms-for-finding-the-optimal-values-in-a-simulated-environment-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159906.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27211</span> An Optimal Bayesian Maintenance Policy for a Partially Observable System Subject to Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei%20Ghosheh%20Balagh">Akram Khaleghei Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new maintenance model for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model. A cost-optimal Bayesian control policy is developed for maintaining the system. The control problem is formulated in the semi-Markov decision process framework. An effective computational algorithm is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control" title=" multivariate Bayesian control"> multivariate Bayesian control</a> </p> <a href="https://publications.waset.org/abstracts/12740/an-optimal-bayesian-maintenance-policy-for-a-partially-observable-system-subject-to-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27210</span> A Controlled Mathematical Model for Population Dynamics in an Infested Honeybees Colonies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chakib%20Jerry">Chakib Jerry</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Jerry"> Mounir Jerry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a mathematical model of infested honey bees colonies is formulated in order to investigate Colony Collapse Disorder in a honeybee colony. CCD, as it is known, is a major problem on honeybee farms because of the massive decline in colony numbers. We introduce to the model a control variable which represents forager protection. We study the controlled model to derive conditions under which the bee colony can fight off epidemic. Secondly we study the problem of minimizing prevention cost under model’s dynamics constraints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=honey%20bee" title="honey bee">honey bee</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20transmission%20model" title=" disease transmission model"> disease transmission model</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20control%20honeybees" title=" disease control honeybees"> disease control honeybees</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a> </p> <a href="https://publications.waset.org/abstracts/48127/a-controlled-mathematical-model-for-population-dynamics-in-an-infested-honeybees-colonies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27209</span> Optimal Sliding Mode Controller for Knee Flexion during Walking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Sitler">Gabriel Sitler</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Salem"> Asad Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimal and robust sliding mode controller (SMC) to regulate the position of the knee joint angle for patients suffering from knee injuries. The controller imitates the role of active orthoses that produce the joint torques required to overcome gravity and loading forces and regain natural human movements. To this end, a mathematical model of the shank, the lower part of the leg, is derived first and then used for the control system design and computer simulations. The design of the controller is carried out in optimal and multi-objective settings. Four objectives are considered: minimization of the control effort and tracking error; and maximization of the control signal smoothness and closed-loop system’s speed of response. Optimal solutions in terms of the Pareto set and its image, the Pareto front, are obtained. The results show that there are trade-offs among the design objectives and many optimal solutions from which the decision-maker can choose to implement. Also, computer simulations conducted at different points from the Pareto set and assuming knee squat movement demonstrate competing relationships among the design goals. In addition, the proposed control algorithm shows robustness in tracking a standard gait signal when accounting for uncertainty in the shank’s parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20control" title=" sliding mode control"> sliding mode control</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable%20knee%20exoskeletons" title=" wearable knee exoskeletons"> wearable knee exoskeletons</a> </p> <a href="https://publications.waset.org/abstracts/164514/optimal-sliding-mode-controller-for-knee-flexion-during-walking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27208</span> Effect of Variable Fluxes on Optimal Flux Distribution in a Metabolic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Motamedian">Ehsan Motamedian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flux%20variability" title="flux variability">flux variability</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20network" title=" metabolic network"> metabolic network</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20optimal%20solutions" title=" multiple optimal solutions"> multiple optimal solutions</a> </p> <a href="https://publications.waset.org/abstracts/15698/effect-of-variable-fluxes-on-optimal-flux-distribution-in-a-metabolic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15698.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27207</span> Multi-Objective Optimal Design of a Cascade Control System for a Class of Underactuated Mechanical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuekun%20Chen">Yuekun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Sardahi"> Yousef Sardahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salam%20Hajjar"> Salam Hajjar</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Greer"> Christopher Greer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cascade%20control" title="cascade control">cascade control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-Loop%20control%20systems" title=" multi-Loop control systems"> multi-Loop control systems</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a> </p> <a href="https://publications.waset.org/abstracts/113986/multi-objective-optimal-design-of-a-cascade-control-system-for-a-class-of-underactuated-mechanical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113986.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27206</span> Modeling and Optimal Control of Hybrid Unmanned Aerial Vehicles with Wind Disturbance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sunsoo%20Kim">Sunsoo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Niladri%20Das"> Niladri Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Raktim%20Bhattacharya"> Raktim Bhattacharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses modeling and control of a six-degree-of-freedom unmanned aerial vehicle capable of vertical take-off and landing in the presence of wind disturbances. We design a hybrid vehicle that combines the benefits of both the fixed-wing and the rotary-wing UAVs. A non-linear model for the hybrid vehicle is rapidly built, combining rigid body dynamics, aerodynamics of wing, and dynamics of the motor and propeller. Further, we design a H₂ optimal controller to make the UAV robust to wind disturbances. We compare its results against that of proportional-integral-derivative and linear-quadratic regulator based control. Our proposed controller results in better performance in terms of root mean squared errors and time responses during two scenarios: hover and level- flight. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20UAVs" title="hybrid UAVs">hybrid UAVs</a>, <a href="https://publications.waset.org/abstracts/search?q=VTOL" title=" VTOL"> VTOL</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20modeling" title=" aircraft modeling"> aircraft modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=H2%20optimal%20control" title=" H2 optimal control"> H2 optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20disturbances" title=" wind disturbances"> wind disturbances</a> </p> <a href="https://publications.waset.org/abstracts/126426/modeling-and-optimal-control-of-hybrid-unmanned-aerial-vehicles-with-wind-disturbance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimal%20control%20model&page=5">5</a></li> <li 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