Open AccessNeural network‐based optimal tracking control for partially unknown discrete‐time non‐linear systems using reinforcement learning
Author(s) -
Zhao Jingang,
Vishal Prateek
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12037
Subject(s) - hamilton–jacobi–bellman equation , reinforcement learning , control theory (sociology) , artificial neural network , optimal control , discrete time and continuous time , mathematics , tracking (education) , lyapunov function , computer science , mathematical optimization , nonlinear system , control (management) , artificial intelligence , psychology , pedagogy , statistics , physics , quantum mechanics
Otimal tracking control of discrete‐time non‐linear systems is investigated in this paper. The system drift dynamics is unknown in this investigation. Firstly, in the light of the discrete‐time non‐linear systems and reference signal, an augmented system is constructed. Optimal tracking control problem of original non‐linear systems is thus transformed into solving optimal regulation problem of the augmented systems. The solution to optimal regulation problem can be found by solving its Hamilton–Jacobi–Bellman (HJB) equation. To solve the HJB equation, a new critic‐actor neural network (NN) structure‐based online reinforcement learning (RL) scheme is proposed to learn the solution of HJB equation while the corresponding optimal control input that minimizes the HJB equation is calculated in a forward‐in‐time manner without requiring any value, policy iterations and the system drift dynamics. The Uniformly Ultimately Boundedness (UUB) of NN weight errors and closed‐loop augmented system states are provided via the Lyapunov theory. Finally, simulation results are given to validate the proposed scheme.