A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

D. Ha Vu, Shoudao Huang, T. Diep Tran, T. Yen Vu, V. Cuong Pham


In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.


adaptive control, fuzzy neural networks, robot manipulators, unknown dead-zone

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DOI: https://doi.org/10.15837/ijccc.2019.5.3621

Copyright (c) 2019 Ha Vu Duc

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