Title
Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.
Abstract
In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton–Jacobi–Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration.
Year
DOI
Venue
2018
10.1016/j.neunet.2017.09.005
Neural Networks
Keywords
Field
DocType
Adaptive critic designs,Dynamical uncertainty,Learning systems,Neural networks,Optimal control,Robust tracking control
Control theory,Mathematical optimization,Nonlinear system,Optimal control,Control theory,Computer science,Adaptive control,Robust control,Artificial neural network,Trajectory,Tracking error
Journal
Volume
Issue
ISSN
97
1
0893-6080
Citations 
PageRank 
References 
3
0.42
23
Authors
4
Name
Order
Citations
PageRank
Ding Wang1187068.16
Derong Liu25457286.88
Yun Zhang357630.23
Hongyi Li44084120.76