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Proceedings Paper

A Lyapunov-based model reference control scheme with CMAC neural network
Author(s): Hongjie Hu; Yuqiao Miao
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Paper Abstract

This paper presents a novel model reference control scheme. The CMAC(Cerebellar Model Articulation Controller) neural network is used to minimize the differences between the reference model and the plant which is influenced because of parameter variation and disturbance online. Moreover the neural network iterative algorithm based on Lyapunov stability is provided in the paper. By this, the output of the plant can accurately follow the the nominal model, videlicet the plant has a characteristic of linear and certain, whose dynamic performance is the same as the nominal model's. So using the traditional control methods can make the system have perfect transient and steady-state performance. Simulation results demonstrate that the proposed control scheme can reduce the plant's sensitivity to parameter variation and disturbance.

Paper Details

Date Published: 30 October 2006
PDF: 6 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63583R (30 October 2006); doi: 10.1117/12.718157
Show Author Affiliations
Hongjie Hu, Beihang Univ. (China)
Yuqiao Miao, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

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