A fuzzy neural network controller with adaptive learning rates for nonlinear slider-crank mechanism |
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Authors: | Rong-Jong Wai Faa-Jeng Lin |
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Affiliation: | Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 32023, Taiwan |
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Abstract: | A fuzzy neural network (FNN) controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN controller with adaptive learning rates is demonstrated by simulation and experimental results. |
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Keywords: | Fuzzy neural network Adaptive learning rates Synchronous motor Position control Slider-crank mechanism |
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