Paper Info
Title
Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning.
Abstract
This paper presents a data-based robust adaptive control methodology for a class of nonlinear constrained-input systems with completely unknown dynamics. By introducing a value function for the nominal system, the robust control problem is transformed into a constrained optimal control problem. Due to the unavailability of system dynamics, a data-based integral reinforcement learning (RL) algorithm is developed to solve the constrained optimal control problem. Based on the present algorithm, the value function and the control policy can be updated simultaneously using only system data. The convergence of the developed algorithm is proved via an established equivalence relationship. To implement the integral RL algorithm, an actor neural network (NN) and a critic NN are separately utilized to approximate the control policy and the value function, and the least squares method is employed to estimate the unknown parameters. By using Lyapunov's direct method, the obtained approximate optimal control is verified to guarantee the unknown nonlinear system to be stable in the sense of uniform ultimate boundedness. Two examples are provided to demonstrate the effectiveness and applicability of the theoretical results.
Year
DOI
Venue
2016
10.1016/j.ins.2016.07.051
Inf. Sci.
Keywords
Field
DocType
Adaptive dynamic programming,Input constraint,Neural networks,Optimal control,Reinforcement learning,Robust control
Nonlinear system,Control theory,Artificial intelligence,Robust control,Artificial neural network,Reinforcement learning,Lyapunov function,Mathematical optimization,Optimal control,Bellman equation,Adaptive control,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
369
C
0020-0255
Citations 
PageRank 
References 
8
0.47
0
Authors
4
Name
Order
Citations
PageRank
Xiong Yang129411.84
Derong Liu25457286.88
Biao Luo355423.80
Chao Li4525110.37