A lateral control strategy for unmanned ground vehicles with model predictive control and active disturbance rejection control

This paper presents a lateral control strategy with kinematic state error model-based predictive control and extended state observer for unmanned ground vehicles. Firstly, we propose a circular arc prediction technique to calculate the state of the reference system. Then, inspired by the idea of active disturbance rejection control, an extended state observer is utilized to estimate the value of the total disturbance caused by modeling uncertainties, external disturbance, and other factors in order to compensate model error. Finally, we propose a lateral controller that combines model-based prediction with extended state observer through state feedback to achieve precise trajectory tracking. The performance of the proposed control strategy is demonstrated by a co-simulation between CarSim and MATLAB/Simulink.