DocumentCode :
295883
Title :
Adaptive learning control of affine nonlinear systems using piecewise linearly trained networks
Author :
Choi, Jin Young ; Park, Hyun Joo
Author_Institution :
Sch. of Electr. Eng., Seoul Nat. Univ., South Korea
Volume :
5
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
2338
Abstract :
This paper presents an approach of online adaptive learning control for a discrete-time affine nonlinear system with relative degree greater than one. For the system identification and control computation, we use a universal approximation model employing self-organizing and piecewise linear fitting techniques for fast training. The computational load for adaptation of our approach is similar to that of the linear adaptive control. Moreover, the present controller retains the trained control information about nonlinear systems with time varying operating points. As a result, unlike the linear adaptive control, the present control system can quickly adapts itself to any situation similar to the previously trained one. The effectiveness of our method is demonstrated by simulations
Keywords :
adaptive control; discrete time systems; identification; intelligent control; neural nets; neurocontrollers; nonlinear control systems; piecewise-linear techniques; real-time systems; SISO systems; adaptive learning control; affine nonlinear systems; discrete-time systems; identification; neural networks; piecewise linear fitting; piecewise linearly trained networks; real time systems; universal approximation model; Adaptive control; Control system synthesis; Control systems; Nonlinear control systems; Nonlinear systems; Piecewise linear approximation; Piecewise linear techniques; Programmable control; System identification; Time varying systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487726
Filename :
487726
Link To Document :
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