Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

This study presents a novel event-triggered stochastic finite-time tracking control method for a robot manipulator. A stochastic dynamic model is constructed to depict the random moment of inertia of the manipulator system, and the parameter disturbance is estimated by using a stochastic configuration neural network. An event-triggered controller with uncertain disturbance rejection is proposed, which realizes the stochastic stability of the tracking error system with finite-time convergence, safe motion velocity, and high tracking accuracy. Compared with the existing works, the obvious feature of the proposed controller is that it can simultaneously restrain the random disturbance and uncertain parameter disturbance, save communication resources, and ensure that the manipulator system can reach a steady state in finite time. We also discuss the effectiveness of the proposed stochastic tracking control method by simulation-based comparative analysis and experimental study, and the results show that the controller can be updated less frequently while guaranteeing robust tracking performance of the robot manipulator.