Adaptive Fuzzy-Neural Control Utilizing Sliding Mode Based Learning Algorithm for Robot Manipulator
نویسندگان
چکیده
This paper introduces an adaptive fuzzy-neural control (AFNC) utilizing sliding mode-based learning algorithm (SMBLA) for robot manipulator to track the desired trajectory. A traditional sliding mode controller is applied to ensure the asymptotic stability of the system, and the fuzzy rule-based wavelet neural networks (FWNNs) are employed as the feedback controllers. Additionally, a novel adaptation of the FWNNs parameters is derived from the SMBLA in the Lyapunov stability theorem. Hence, the AFNC approximates parameter variation, unmodeled dynamics, and unknown disturbances without the detailed knowledge of robot manipulator, while resulting in an improved tracking performance. Lastly, in order to validate the effectiveness of the proposed approach, the comparative simulation results of two-degrees of freedom robot manipulator are presented.
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