NDLI: A General Learning Scheme for CMAC-based Controller

Content Provider	Springer Nature Link
Author	Jiang, Zhiming Wang, Shengwei
Copyright Year	2003
Abstract	Cerebellar model articulation controller (CMAC) is a powerful tool for nonlinear control applications. However, it yet lacks an adequate learning scheme. It is found that, with the existing learning scheme, if a complicated learning algorithm is not used, CMAC can destabilize a system that is otherwise stable. Oscillations resulting from the interaction between CMAC and the classical controller were found to contribute to the instability. This paper presents a new CMAC learning scheme that models plant's characteristics based on closed loop errors instead of the original input-output pairs. In this scheme, memory space of the CMAC is partitioned into two parts. One is for dynamic control, in which dynamic information is stored. Another is for steady state control, in which steady state information is adaptively updated for smooth control. Relationship between the two parts of the space is discussed and specified for a stable control. Simulation results on a typical nonlinear plant model and a real electrohydraulic servo system using the proposed scheme demonstrate that the oscillations are eliminated and stable control is obtained. The new scheme demonstrates superior tracking performance, noise rejection property and good robustness.
Starting Page	125
Ending Page	138
Page Count	14
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	18
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2003-01-01
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Artificial Intelligence (incl. Robotics) Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks Electronic and Computer Engineering Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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