| Summary: | 碩士 === 大同大學 === 電機工程學系(所) === 101 === In this thesis, the self-constructing fuzzy neural network controller (SCFNN) using recurrent neural network (RNN) learning strategy is proposed. For back-propagation (BP) algorithm of the SCFNN controller, the exact calculation of the Jacobian of the system cannot be determined. In this thesis, the RNN learning strategy is proposed to replace the error term of SCFNN controller. After the training of the RNN learning strategy, that will receive the relation between controlling signal and result of the nonlinear of the plant completely. Moreover, the structure and the parameter-learning phases are preformed concurrently and on-line in the SCRFNN. The SCFNN controller is designed to achieve the tracking control of an electronic throttle. The proposed controller, there are two processes that one is structure learning phase and another is parameter learning phase. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-decent method using BP algorithm. Mahalanobis distance (M-distance) method in this thesis is employed as the criterion to identify the Gaussian function will be generated / eliminated or not. Finally, the simulation results of the electronic throttle valve are provided to demonstrate the performance and effectiveness of the proposed controller.
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