An Improve Nonlinear Robust Control Approach for Robotic Manipulators with PSO-based Global Optimization Strategy

Abstract During the trajectory tracking process of robotic manipulators, many factors including complex strong nonlinear dynamics, unknown uncertainties and external disturbances, greatly increase the difficulty of control effect. Aiming for this issue, a nonlinear active disturbance rejection controller (NADRC) is proposed for robotic manipulators. In this controller, an extended state observer is introduced on basis of the dynamic model, to observe the extend state of model uncertainties and external disturbances. Then, in combination with the nonlinear feedback control structure, the robust trajectory tracking of robotic manipulators is achieved. Furthermore. In order to optimize the key parameters of the controller, an improved particle swarm optimization algorithm (IPSO) was designed using chaos theory, which improves the tracking accuracy of the proposed NDRC strategy effectively. Finally, the effectiveness of the proposed control strategy was verified through simulation experiments.