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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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica Vol. 8; no. 4; pp. 766 - 778
Main Authors: Wang, Xueli, Ding, Derui, Dong, Hongli, Zhang, Xian-Ming
Format: Journal Article
Language:English
Published: 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
Subjects:
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
neural network (NN)
constrained inputs
stochastic communication protocols (SCPs)
Adaptive dynamic programming (ADP)
suboptimal control
ISSN:2329-9266, 2329-9274
Online Access:Get full text
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Summary: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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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2021.1003922