This paper presents an adaptive neural net controller for controlling given plants which are unknown. In the neural net structure, a two-layered network is used to emulate the unknown plant dynamics, and another two-layer neural network, which is the inverse of the estimator, is used to generate the control action on-line. A modified Widrow-Hoff delta rule is adopted as a learning algorithm to minimize the error between the real plant response and the output of the estimator. An effective learning method which is based on sliding motions is provided to tune the control action to improve the system performance and convergence. The major advantage of the proposed approach is that the lengthy training of the controller might be eliminated. The effectiveness of the proposed approach is illustrated through simulations of controlling a unstable plant and a normalized motor model with noise disturbances.
|Number of pages||7|
|Publication status||Published - 1995|
|Event||Proceedings of the 1995 International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies - Taipei, Taiwan|
Duration: 1995 May 22 → 1995 May 27
|Conference||Proceedings of the 1995 International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies|
|Period||1995/05/22 → 1995/05/27|
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