A minimum variance predictive controller for nonlinear systems

Author

Qingbo, Shen ; Jin, Wang ; Chang, LI

Author_Institution

Dept. of Autom., Fushun Petrol. Inst., China

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

741

Abstract

The paper presents a minimum variance predictive controller (MVPC) using a modified neural network (MNN) in order to learn the characteristics of a dynamic system. The MVPC can adapt parameter variation and uncertainty in the controlled plant through online learning. The learning algorithm is considerably faster because of the introduction of a recursive least squares (RLS) algorithm. Simulation results show that the proposed approach is effective for adaptive control of nonlinear systems

Keywords

adaptive control; learning (artificial intelligence); least squares approximations; neurocontrollers; nonlinear control systems; predictive control; MVPC; adaptive control; dynamic system; learning algorithm; minimum variance predictive controller; modified neural network; nonlinear systems; online learning; parameter variation; recursive least squares; uncertainty; Adaptive control; Control systems; Least squares methods; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Resonance light scattering; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672886

Filename

672886