Dongsheng Ma

This paper investigates the finite-time-trajectory tracking control of a robotic manipulator subject to time-varying output constraints and unknown lumped disturbances consisting of model uncertainties and external forces. A novel finite-time integral barrier Lyapunov function (FT-IBLF) with an adjustable parameter is constructed. Its barrier property and fractional-power finite-time inequality are established, which ensures that the position tracking error is strictly limited within the preset time-varying constraint boundary. Secondly, a backstepping-based finite-time tracking controller is designed, in which a finite-time disturbance observer is incorporated to estimate the lumped disturbance and compensate for it in a feedforward manner, thereby reducing the dependence on exact model information. Finally, simulations demonstrate that the proposed method can converge the tracking error to the neighborhood of zero within a finite time, and the system state never violates the constraint boundary. The dynamic performance of the system can be flexibly adjusted and optimized by adjusting parameters, which also verifies the effectiveness and superiority of this method.

A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation.