Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

Abstract

This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations.