A Novel Faster Fixed-Time Adaptive Control for Robotic Systems With Input Saturation


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In this article, an adaptive antisaturation fixed-time control method with a faster convergence rate is studied for uncertain robotic systems. First, a new segmental sliding variable is constructed to solve the singularity problem brought by the terminal sliding mode control (TSMC) and achieve a faster convergence rate. Second, to approximate and compensate for the model uncertainty and the viscous friction parameter, an adaptive neural network (ANN) is employed. Then, a novel auxiliary system is constructed to mitigate the effects of input saturation. Based on this, a novel nonsingular TSMC algorithm integrated with the ANN and the auxiliary system is designed, so that the trajectory tracking errors of the robotic system can converge within a faster fixed time with actuator saturation. Finally, the superiority and practicability of the present method are verified by comparative experiments.
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Key words
Fixed-time control,input saturation,neural network,robotic systems,sliding mode control (SMC)
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