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Abstract

This study proposes a fuzzy Cerebellar Model Articulation Controller (CMAC) using a dynamic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique for dealing with the metallic sphere position control of a magnetic levitation system (MLS). The proposed Dynamic TOPSIS Fuzzy CMAC (DTFCMAC) incorporates a multi-criteria decision analysis with a fuzzy structure to decrease the computational load for parameter learning and to enhance the fuzzy reasoning inference for a CMAC. The Shannon entropy index is used to derive the objective weights for the evaluation criterion. By combining entropy weight and TOPSIS, the optimal threshold value for suitable firing nodes is determined automatically and easily. In the proposed method, the dynamic back-propagation algorithm is applied to train the proposed DTFCMAC online. Moreover, to guarantee the convergence of output tracking error for periodic command tracking, analytical methods developed from a discrete-type Lyapunov function are used to determine the optimal learning-rate parameters for the proposed DTFCMAC. The proposed DTFCMAC is applied to the MLS, and its performance is verified through simulations and experiments. Our findings indicate that the proposed DTFCMAC control system achieves stability and desired control performance for the MLS.

Keywords

Dynamic TOPSIS entropy fuzzy inference system cerebellar model articulation controller and magnetic levitation system

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Dynamic TOPSIS fuzzy cerebellar model articulation controller for magnetic levitation system

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