Dual adaptive control of nonlinear stochastic systems based on echo state network

Author

Suping Cao ; Wenxia Xu ; Xizhen Hu

Author_Institution

Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2015

fDate

23-25 May 2015

Firstpage

4579

Lastpage

4584

Abstract

The dual adaptive control problem is addressed for a class of Single-In-Single-Out (SISO) stochastic, affine nonlinear, discrete systems. The nonlinear functions of system model are assumed to be unknown and approximated by the echo state networks (ESNs). The parameters of ESNs are online adjusted using the conventional Kalman filtering technique. The dual adaptive control law is designed considering an explicit-type, suboptimal cost function based on the innovations. The simulations testified the performance of the proposed control law.

Keywords

Kalman filters; adaptive control; control system synthesis; discrete systems; neurocontrollers; nonlinear control systems; recurrent neural nets; stochastic systems; ESN; Kalman filtering technique; SISO system; cost function; discrete system; dual adaptive control law design; echo state network; nonlinear stochastic system; single-in-single-out system; system model nonlinear function; Adaptation models; Adaptive control; Approximation methods; Cost function; Kalman filters; Neurons; Stochastic systems; Dual Adaptive Control; Echo State Network; Kalman Filter; Nonlinear Stochastic System;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162732

Filename

7162732