Learning predictive tracking control method with Gaussian process modeling for mobile robots
WU Rui-zhuo
ZHANG Xing-long
XU Xin
ZHANG Chang-xin
Abstract：Due to environmental and model uncertainty,mobile robots face significant challenges in tracking control in complex environments.Dynamic environment,such as meadows,and deep slopes,would result in performance degrada-tion.This paper proposes a learning predictive control method with Gaussian process modeling,that can effectively model and predict environmental and model uncertainty,then design optimal control strategies utilizing the uncertainty model.The paper uses Gaussian process regression to model uncertainty and utilize the model to learn the optimal policy in the receding horizon reinforcement learning algorithm,iterating to learn the optimal control strategy.Aiming at the lateral tracking control problem of wheeled robots on elliptical and eight-shaped trajectories,simulation experiments were car-ried out and compared with nonlinear model predictive control methods.The results indicate that the proposed algorithm effectively enhances the control performance of the controller in complex scenarios,showing a 20％improvement in per-formance indicators compared to receding horizon reinforcement learning method and a 36％improvement in performance indicators compared to nonlinear model predictive control method.This verifies the effectiveness and superiority of the proposed method.
Keywords：Guassian processlearning predictive controlreceding horizon reinforcement learningenvironment and model uncertaintycontrol technique of unmanned system
Publication Date：2023-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 2236-2246 )