A novel nonlinear adaptive data driven control strategy

Author

Dong Na ; Wu Ai-Guo

Author_Institution

Tianjin Key Lab. of Process Meas. & Control, Tianjin Univ., Tianjin, China

fYear

2013

Firstpage

386

Lastpage

390

Abstract

A novel adaptive data driven control strategy is proposed for general discrete nonlinear systems. The controller is designed based on the Simultaneous Perturbation Stochastic Approximation (SPSA) method, and is constructed through use of a Function Approximator (FA), which is fixed as a neural network here. In this novel approach, the parametric estimation is designed to be adaptive, which greatly improves the control ability. The proposed control strategy is finally applied to solve nonlinear tracking problems for discrete-time nonlinear systems. The feasibility and effectiveness of the proposed adaptive data driven control strategy is well demonstrated through Simulation comparison tests.

Keywords

adaptive control; approximation theory; control system synthesis; discrete time systems; neurocontrollers; nonlinear control systems; parameter estimation; stochastic processes; FA; SPSA; discrete-time nonlinear systems; function approximator; neural network; nonlinear adaptive data driven control strategy; nonlinear tracking problems; parametric estimation; simulation comparison tests; simultaneous perturbation stochastic approximation method; Adaptation models; Adaptive systems; Approximation methods; Control systems; Mathematical model; Neural networks; Nonlinear systems; Adaptive data driven control; Discrete nonlinear systems; Neural networks; Simultaneous Perturbation Stochastic Approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6639462