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Adaptive Fuzzy Wavelet Network Sliding Mode Control for Dual-Robot System With Time Delay and Dynamic Uncertainty

For the position and velocity tracking of multi-input and multi-output nonlinear dual-robot system with time delay and dynamic uncertainty, an adaptive fuzzy wavelet network sliding mode control scheme is proposed. The integral sliding mode is designed as the inputs of the controller to reduce the number of the inputs of the system and the fuzzy rules. Then, for the problems of time delay and unknown nonlinear dynamics of the system, a fuzzy wavelet network controller is designed combing the approximation ability of the fuzzy system with the learning ability of the wavelet network. Moreover, switching control is designed to reduce the approximation error. Finally, the adaptive laws are designed based on the Lyapunov function to adjust the parameters of the controller in real time. The performance of the proposed control is compared to that of the conventional adaptive fuzzy control in the experimental setup with the two Phantom Omni robots. The experimental results show that better joint position and velocity tracking can be achieved under time delay and dynamic uncertainty with the proposed adaptive fuzzy wavelet network sliding mode control.