Finite-Time Tracking Control in Robotic Arm with Physical Constraints Under Disturbances

Abstract

This paper proposes a novel control algorithm for robotic manipulators with unknown nonlinearities and external disturbances. Explicit consideration is given to the physical constraints on joint positions and velocities, ensuring tracking performance without violating prescribed constraints. Finite-time convergence entails significant overshoot magnitudes. A class of nonlinear transformations is employed to ensure state constraint satisfaction while achieving prescribed tracking performance. The command filtered backstepping is employed to circumvent issues of “explosion of terms” in virtual controls. A disturbance observer (DOB), constructed via radial basis function neural networks (RBFNNs), effectively compensates for nonlinearities and time-dependent disturbances. The proposed control law guarantees finite-time stability while preventing position/velocity violations during transients. Simulation results validate the effectiveness of the proposed approach.