Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning  

作　　者：LIU Yuesheng HE Ning HE Lile ZHANG Yiwen XI Kun ZHANG Mengrui 刘月笙;贺宁;贺利乐;张译文;习坤;张梦芮

机构地区：[1]School of Mechatronic Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第6期1028-1036,共9页上海交通大学学报(英文版)

基　　金：the National Natural Science Foundation of China(No.61903291);the Key Research and Development Program of Shaanxi Province(No.2022NY-094)。

摘　　要：Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.

关 键 词：model predictive control(MPC) path tracking mobile robot machine learning parameter tuning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP242[自动化与计算机技术—控制科学与工程]