Title
Optimal tracking control based on reinforcement learning value iteration algorithm for time-delayed nonlinear systems with external disturbances and input constraints
Abstract
This article investigates the design of an optimal tracking controller for a class of nonlinear continuous-time systems with time-delay, mismatched external disturbances and input constraints. The technique of integral reinforcement learning (IRL) is utilized for determining the control input that optimizes an objective function. To enable the usage of an estimation of the external disturbances in the recursive objective function, a disturbance observer is designed. For the derivation of the optimal control input, a Hamilton-Jacobi-Bellman (HJB) equation is employed and solved using the iterative IRL algorithm. The proposed approach guarantees that in the presence of mismatched disturbances, the output of the time-delayed nonlinear system tracks the desired trajectory with bounded error. A critic neural network is designed for the implementation of the proposed approach. The efficiency of the method is illustrated by a simulation example.
Year
DOI
Venue
2021
10.1016/j.ins.2020.11.057
Information Sciences
Keywords
DocType
Volume
Reinforcement learning,Tracking control,Mismatched external disturbance,Time delay,Disturbance observer,Critic neural network,Input constraints
Journal
554
ISSN
Citations 
PageRank 
0020-0255
2
0.37
References 
Authors
0
4
Name
Order
Citations
PageRank
Mehdi Mohammadi1109150.02
M. Arefi27510.75
Peyman Setoodeh321.38
Okyay Kaynak4121.33