自动化学报2025，Vol.51Issue(11)：2427-2440,14.DOI:10.16383/j.aas.c250207
基于随机森林学习残差的重载卡车模型预测控制器设计
Model Predictive Controller Design for Heavy-duty Trucks Based on Random Forest Residual Learning
摘要
Abstract
In recent years,model predictive control(MPC)has been widely applied in the field of autonomous driv-ing,yet still faces challenges in nonlinear vehicle dynamics modeling and real-time rolling optimization.Data-driven MPC,which establishes vehicle dynamics models by collecting the input-output data of the system to directly learn the dynamics model,still requires an additional processing step to address the real-time rolling optimization prob-lem.To tackle this issue,a method for learning the vehicle dynamics model based on random forest is proposed.The vehicle dynamics model is decomposed into a nominal model and a residual model,with a two-layer random forest learning the residual model.The upper layer is used to switch between different linear models while the lower layer fits linear models at leaf nodes.Since both nominal and residual models are linear models,the rolling optimiza-tion can be directly solved in real time using quadratic programming solvers.Meanwhile,the residual model based on random forest uses multi-frame historical states as feature inputs,and the learned residual model retains the delay characteristics of the dynamic response of the dynamics system,thus effectively mitigating the impact of delays.Simulation test and real-world vehicle experimental results demonstrate that the proposed MPC achieves su-perior tracking accuracy and real-time performance compared to nominal model MPC and Gaussian process-based MPC,and exhibits excellent adaptability to vehicle actuator delays.关键词
模型预测控制/残差学习/随机森林/轨迹跟踪/延迟Key words
Model predictive control/residual learning/random forest/trajectory tracking/delay引用本文复制引用
赵康,李小凡,薛建儒..基于随机森林学习残差的重载卡车模型预测控制器设计[J].自动化学报,2025,51(11):2427-2440,14.基金项目
国家自然科学基金(62036008),国家重点研发计划(2024YFE0210700)资助Supported by National Natural Science Foundation of China(62036008)and National Key Research and Development Pro-gram of China(2024YFE0210700) （62036008）
