Adaptive learning control for nonminimum phase systems

Author

Wang, X.Z. ; Chen, D.J.

Author_Institution

Iowa State Univ., Ames, IA, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

26

Abstract

A novel adaptive learning algorithm is presented for the repetitive tracking control of a class of unstable nonminimum phase systems. After each repetitive trial, a least squares method is used to estimate the system parameters. The output tracking error and the identified system model are used through stable inversion to find the feed forward input, together with the desired state trajectories for the next trial. An adaptive backstepping based tracking controller is used in each trial to ensure the regulation of the desired state trajectories. Simulation results demonstrate that the proposed learning control scheme is very effective in reproducing the desired trajectories

Keywords

adaptive control; feedforward; intelligent control; learning (artificial intelligence); least squares approximations; position control; tracking; adaptive backstepping based tracking controller; adaptive learning algorithm; adaptive learning control; feed forward input; identified system model; learning control scheme; least squares method; nonminimum phase systems; output tracking error; repetitive tracking control; repetitive trial; stable inversion; state trajectories; state trajectory regulation; system parameter estimation; Adaptive control; Backstepping; Control systems; Error correction; Feeds; Iterative algorithms; Parameter estimation; Programmable control; Robots; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.884959

Filename

884959