Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation
针对参数不确定和输入饱和的机器人系统，提出一种鲁棒自适应固定时间滑模控制方法，结合神经网络和终端滑模，提高鲁棒性和收敛速度，仿真验证了有效性。
In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.