A barrier composite energy function approach for robot manipulators under alignment condition with position constraints

In this work, we propose an iterative learning control scheme with a novel barrier composite energy function approach to deal with position constrained robotic manipulators with uncertainties under alignment condition. The classical assumption of initial resetting condition is removed. Through rigorous analysis, we show that uniform convergence is guaranteed for joint position and velocity tracking error. By introducing a novel tan-type barrier Lyapunov function into barrier composite energy function and keeping it bounded in closed-loop analysis, the constraint on joint position vector will not be violated. A simulation study has further demonstrated the efficacy of the proposed scheme.