A Zeroing Neural Dynamics-Based Predictive Control Algorithm for Manipulator Trajectory Tracking and Obstacle Avoidance

Abstract

To address the problem of robotic manipulator trajectory tracking in complex environments subject to obstacle constraints and multilevel joint constraints, this paper proposes a trajectory tracking and obstacle avoidance control method based on model predictive control (MPC) and zeroing neural dynamics (ZND). First, a unified optimization model is established by simultaneously considering trajectory tracking error, control input smoothness, obstacle avoidance requirements, and multilevel joint constraints, so as to achieve safe motion control of the manipulator in complex environments. Then, for the resulting time-varying optimization problem, a corresponding ZND-based online solver is designed to dynamically update the optimization variables in real time, thereby improving the computational efficiency of MPC and satisfying the real-time requirement of the control system. The proposed method enables the endeffector to accurately track the desired trajectory while effectively avoiding collisions with obstacles and satisfying multilevel joint constraints. Simulation results demonstrate that the proposed method achieves favorable performance in terms of tracking accuracy, obstacle avoidance safety, and online computational efficiency.