Multi-Innovation Gradient Parameter Estimation Based Adaptive Control for Discrete-Time Systems

Author

Zhang, Jiabo ; Ding, Feng ; Shi, Yang

Author_Institution

Jiangnan (Southern Yangtze) Univ., Wuxi

fYear

2007

fDate

18-21 Aug. 2007

Firstpage

399

Lastpage

404

Abstract

This paper uses the multi-innovation stochastic gradient (MISG) algorithm to estimate the parameters of discrete- time systems and presents an MISG based adaptive control scheme. Further, we prove that the parameter estimation error converges to zero under the persistent excitation, and the parameter estimation based control algorithm can achieve virtually asymptotically optimal control and ensure that the closed-loop systems are stable and globally convergent. The simulation results are included.

Keywords

adaptive control; asymptotic stability; closed loop systems; convergence; discrete time systems; gradient methods; optimal control; parameter estimation; stochastic processes; adaptive control; closed-loop systems; discrete-time systems; multiinnovation stochastic gradient algorithm; parameter estimation; persistent excitation; stability; virtually asymptotically optimal control; Adaptive control; Control systems; Convergence; Equations; Error correction; Optimal control; Parameter estimation; Polynomials; Stochastic processes; Stochastic systems; System identification; adaptive control; parameter estimation; stochastic gradient;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2007 IEEE International Conference on

Conference_Location

Jinan

Print_ISBN

978-1-4244-1531-1

Type

conf

DOI

10.1109/ICAL.2007.4338595

Filename

4338595