Event-triggered based adaptive neural network control of a robotic manipulator with output constraints and disturbance

This paper studies event-triggered based adaptive neural network (NN) tracking control of a robotic manipulator with output constraints and disturbance. First, a novel asymmetric tan-type barrier Lyapunov function (BLF) is developed to satisfy the requirement of time-varying output constraints. Then, a fixed threshold event triggering is proposed to reduce the energy consumption, which avoids the happening of Zeno behaviour after analysis. Further, a disturbance observer (DO) and an adaptive neural network are devised to estimate the bounded disturbance and the unknown dynamics of the robotic manipulator. The proposed controller can achieve uniform boundness of the solution and adjustment of transient performance. Finally, the effectiveness of the presented methods is verified by related simulation results.