Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input. (September 2017)

• Record Type: Journal Article
• Authors: Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali
• Is Part Of: Neural networks. Volume 93(2017)
• Page Start: 195
• Page End: 204
• Publication Date: 2017-09
• Subjects: Adaptive control -- Optimal control -- Neural networks -- Nonaffine nonlinear systems; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.05.013
• Languages: English
• ISSNs: 0893-6080

Abstract

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.