Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol
In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are a...
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| Vydané v: | IEEE/CAA journal of automatica sinica Ročník 8; číslo 4; s. 766 - 778 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
Chinese Association of Automation (CAA)
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China%School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Victoria, Australia%Institute of Complex Systems and Advanced Control, Northeast Petroleum University, Daqing 163318, China |
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| ISSN: | 2329-9266, 2329-9274 |
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| Abstract | In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme. |
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| AbstractList | In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme. |
| Author | Ding, Derui Dong, Hongli Zhang, Xian-Ming Wang, Xueli |
| AuthorAffiliation | Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China%School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Victoria, Australia%Institute of Complex Systems and Advanced Control, Northeast Petroleum University, Daqing 163318, China |
| AuthorAffiliation_xml | – name: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China%School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Victoria, Australia%Institute of Complex Systems and Advanced Control, Northeast Petroleum University, Daqing 163318, China |
| Author_xml | – sequence: 1 givenname: Xueli surname: Wang fullname: Wang, Xueli email: xuelywang@163.com organization: University of Shanghai for Science and Technology,Department of Control Science and Engineering,Shanghai,China,200093 – sequence: 2 givenname: Derui surname: Ding fullname: Ding, Derui email: dding@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology,Melbourne,Victoria,Australia,3122 – sequence: 3 givenname: Hongli surname: Dong fullname: Dong, Hongli email: shiningdhl@vip.126.com organization: Institute of Complex Systems and Advanced Control, Northeast Petroleum University,Daqing,China,163318 – sequence: 4 givenname: Xian-Ming surname: Zhang fullname: Zhang, Xian-Ming email: xianmingzhang@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology,Melbourne,Victoria,Australia,3122 |
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| SubjectTerms | Actuators Adaptive dynamic programming (ADP) Adaptive systems Algorithms Artificial neural networks Closed loop systems Communication constrained inputs Control systems Control theory Discrete time systems Dynamic programming Dynamic stability Dynamical systems Feedback control Heuristic algorithms Iterative methods Liapunov functions neural network (NN) Neural networks Nonlinear control Nonlinear dynamical systems Nonlinear dynamics Nonlinear systems Optimal control Performance indices Protocol Protocol (computers) Protocols Robustness (mathematics) Saturation Schedules Stability analysis stochastic communication protocols (SCPs) suboptimal control Switching System identification |
| Title | Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol |
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