Adaptive Neural Control for Robotic Manipulators Under Constrained Task Space

A fundamental requirement in human-robot interaction is the capability for motion in the constrained task space. The control design for robotic manipulators is investigated in this paper, subject to uncertainties and constrained task space. The neural networks (NN) are employed to estimate the uncertainty of robotic dynamics, while the integral barrier Lyapunov Functional (iBLF) is used to handle the effect of constraint. With the proposed control strategy, the system output can converge to an adjustable constrained space without violating the predefined constrained region. Semi-globally uniformly ultimate boundedness of the closed-loop system is guaranteed via Lyapunov’s stability theory. Simulation examples are provided to illustrate the performance of the proposed strategy.