DocumentCode :
2412038
Title :
A new neural network and pole placement based adaptive composite controller
Author :
Hussain, Amir ; Zayed, Ali S. ; Smith, L.S.
Author_Institution :
Dept. of Comput. Sci. & Math., Stirling Univ., UK
fYear :
2001
fDate :
2001
Firstpage :
267
Lastpage :
271
Abstract :
The paper describes a new composite control method combining a neural network estimator with a conventional pole-placement based adaptive controller. The neural network estimation technique presented by Hussain (2000) is particularly effective when there is no complete plant information, or when considering a controlled plant as a ´black box´. In the proposed composite controller, the neural network estimator weights are adapted online to minimise the identification error, and these weights are fed into a robust self-tuning PID controller which provides an adaptive mechanism to ensure that the closed loop poles are placed at the desired positions. Simulation results show that the proposed method applies to general linear or nonlinear control systems.
Keywords :
adaptive control; closed loop systems; identification; neurocontrollers; pole assignment; three-term control; PID controller; adaptive control; closed loop poles; composite control; identification error; minimum variance controller; neural network estimator; pole-placement; robust control; Adaptive control; Control system synthesis; Equations; Neural networks; Nonlinear control systems; Polynomials; Programmable control; Robust control; State feedback; Three-term control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century. Proceedings. IEEE International
Print_ISBN :
0-7803-7406-1
Type :
conf
DOI :
10.1109/INMIC.2001.995349
Filename :
995349
Link To Document :
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