Title
Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.
Abstract
In this paper, an identifier–critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton–Jacobi–Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs’ weights, identification error, and system states is guaranteed using Lyapunov’s direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy.
Year
DOI
Venue
2017
10.1016/j.neunet.2017.05.013
Neural Networks
Keywords
Field
DocType
Adaptive control,Optimal control,Neural networks,Nonaffine nonlinear systems
Hamilton–Jacobi–Bellman equation,Nonlinear system,Control theory,Computer science,System dynamics,Artificial intelligence,Artificial neural network,Lyapunov function,Mathematical optimization,Control theory,Optimal control,Adaptive control,Machine learning
Journal
Volume
Issue
ISSN
93
1
0893-6080
Citations 
PageRank 
References 
7
0.51
23
Authors
3
Name
Order
Citations
PageRank
Kasra Esfandiari1221.51
Farzaneh Abdollahi221217.16
Heidar Ali Talebi317632.23