Robust multiple model adaptive control of uncertain stochastic plant with improved weighting algorithm

Author

Weicun Zhang ; Qing Li ; Yuzhen Zhang ; Yu Meng

Author_Institution

Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2014

fDate

28-30 July 2014

Firstpage

536

Lastpage

541

Abstract

The paper is concerned with the stability of robust multiple model adaptive control systems of continuous-time stochastic plant. First, the traditional weighting algorithm based on multiple Kalman filter and Bayes formulas is replaced by newly developed weighting algorithm; second, with the help of virtual equivalent system concept and methodology, the stability results are presented for linear time-invariant plant and a kind of nonlinear time varying plant.

Keywords

Bayes methods; Kalman filters; adaptive control; continuous time systems; nonlinear control systems; robust control; stochastic systems; uncertain systems; Bayes formulas; continuous-time stochastic plant; improved weighting algorithm; multiple Kalman filter; nonlinear time varying plant; robust multiple model adaptive control system stability; uncertain stochastic plant; virtual equivalent system concept; Adaptation models; Adaptive control; Mathematical model; Robustness; Stability analysis; Stochastic processes; Time-varying systems; Robust multiple model adaptive control; Stability; Virtual equivalent system;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation (ICIA), 2014 IEEE International Conference on

Conference_Location

Hailar

Type

conf

DOI

10.1109/ICInfA.2014.6932713

Filename

6932713