Active Disturbance Rejection Predictive Control for Local Trajectory Planning of Unmanned Ground Vehicles

Abstract

In this paper, an active disturbance rejection predictive control strategy is proposed for trajectory planning task of unmanned ground vehicles. Rather than error estimation of accurate system modeling, internal error and environment disturbance are processed via single extended state observer. Nonlinear feedback control law is applied to reduce steady-state error significantly. Then motion planning and nonholonomic constraints could be handled via nonlinear model predictive control. Simulation and experiment results show that the proposed algorithm is able to accomplish trajectory planning task with internal error and environment disturbance.