Neural-Network-Based Constrained Optimal Control Scheme for Discrete-Time Switched Nonlinear System Using Dual Heuristic Programming

In this paper, a novel iterative two-stage dual heuristic programming (DHP) is proposed to solve the optimal control problems for a class of discrete-time switched nonlinear systems subject to actuators saturation. First, a novel nonquadratic performance functional is introduced to confront control...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 11; no. 3; pp. 839 - 849
Main Authors: Zhang, Huaguang, Qin, Chunbin, Luo, Yanhong
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuator saturation
Actuators
Algorithms
Computer simulation
Control systems
dual heuristic programming
Dynamic programming
Dynamical systems
Equations
Heuristic
IEEE standards
Mathematical analysis
Neural networks
Nonlinear systems
Optimal control
Optimization algorithms
Programming
switched system
Switched systems
Switches
ISSN:1545-5955, 1558-3783
Online Access:Get full text
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Summary:In this paper, a novel iterative two-stage dual heuristic programming (DHP) is proposed to solve the optimal control problems for a class of discrete-time switched nonlinear systems subject to actuators saturation. First, a novel nonquadratic performance functional is introduced to confront control constraints of the saturating actuator. Then, the iterative two-stage DHP algorithm is developed to solve the Hamilton-Jacobi-Bellman (HJB) equation of the switched system with the saturating actuator. Moreover, the convergence and optimality of the two-stage DHP algorithm are strictly proven. To implement this algorithm efficiently, there are two neural networks used as parametric structure to approximate the costate function and the corresponding control law, respectively. Finally, simulation results are given to verify the effectiveness of the proposed algorithm.
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2014.2303139