In response to the challenge of precise trajectory tracking control for robot manipulators,influenced by the accuracy of modeling parameters and disturbance uncertainties,a novel control approach that combines non-singular fast terminal sliding mode control with iterative learning control is presented.First,to ensure the convergence speed of tracking errors and prevent singularity issues during convergence,a non-singular fast terminal sliding mode controller employing the law of approach to saturation is designed.Second,to further improve trajectory tracking accuracy,an error iterative learning controller is developed,and the convergence of these controllers is analyzed.Finally,the control system based on the proposed method is implemented in Simulink for iterative and comparative control simulation experiments.Additionally,real-machine experiments for robot manipulator tracking control are carried out.The experimental results show that:in the iterative experiment,the maximum average steady-state error in joints increases by 72％;in the comparative experiment,compared to PD-type iterative learning control and PD-type linear sliding mode control,the maximum average steady-state error rises by 97％and 51％,respectively,while the maximum response adjustment time decreases by 70％and 50％,respectively;in the real-machine experiment,the robot manipulator tracking error stabilizes within the range of[-0.05,0.05]rad.These findings thoroughly validate the effectiveness and accuracy of the proposed control method,offering an effective control solution for addressing uncertainties in robot manipulator trajectory tracking.
robot manipulatorsnon-singular fast terminal sliding mode controliterative learning controltrajectory tracking
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