The optimal tracking control for Autonomous Underwater Vehicles Using ESO and MPC algorithms

Considering the complexity and uncertainty of the marine environment and the unknown underwater environmental disturbances and rudder angle saturation constraints during the control and tracking process, a control algorithm based on an extended state observer (ESO) combined with model predictive control (MPC) is proposed. Firstly, based on the dynamic characteristics of the AUV, its dynamic model is established, and the AUV plane motion equation and vertical plane equation are obtained by simplification. Secondly, the extended state observer is used to estimate external disturbances, and the model predictive control is used to explicitly handle the rudder angle saturation constraint. The minimization of rudder angle switching and tracking error is taken as the optimization objective. Finally, the control effect is compared with that of widely used classical PID algorithm by simulation experiment. The comparison results show that the proposed algorithm can improve path following effect.