智慧农业（中英文）2024，Vol.6Issue(3)：82-93,12.DOI:10.12133/j.smartag.SA202401010
割草机器人自适应时域MPC路径跟踪控制方法
Adaptive Time Horizon MPC Path Tracking Control Method for Mowing Robot
摘要
Abstract
[Objective]The traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of chang-es in curvature and road bends.This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigat-ing curves and paths.Although extending the time horizon of the standard fixed time horizon model predictive control(MPC)can im-prove curve path tracking accuracy,it comes with high computational costs,making it impractical in situations with restricted comput-ing resources.Consequently,an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing. [Methods]Initially,it was crucial to establish a kinematic model for the mowing robot,which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking.The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing.Second,the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot.The region located a certain distance ahead of the robot's current position,was identified to as the preview region,enabling a more accurate prediction of the robot's future traveling conditions.Calculations for both the curve factor and curve change factor were carried out within this pre-view region.The curvature factor represented the initial curvature of the path,while the curvature change factor indicated the extent of curvature variation in this region.These two variables were then fed into a fuzzy controller,which adjusted the prediction time hori-zon of the MPC.The integration enabled the mowing robot to promptly adjust to changes in the path's curvature,thereby improving its accuracy in tracking the desired trajectory.Additionally,a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance.This approach ensured that MPC activation occurred only when needed,rath-er than at every time step,resulting in reduced computational expenses especially during periods of smooth robot motion where unnec-essary computation overhead could be minimized.By meeting kinematic and dynamic constraints,the optimization algorithm success-fully identified an optimal control sequence,ultimately enhancing stability and reliability of the control system.Consequently,these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex envi-ronments. [Results and Discussion]The adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maxi-mum absolute lateral error to within 0.13 rad and 11 cm,respectively,surpassing the performance of the MPC controller in the control group.Moreover,compared to both the first and fourth groups,the adaptive time-horizon MPC controller achieved a remarkable re-duction of 75.39%and 57.83%in mean values for lateral error and heading error,respectively(38.38%and 31.84%,respectively).Ad-ditionally,it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral er-ror(0.025 6 m)and course error(0.025 5 rad),outperforming all four fixed time-horizon MPC controllers tested in the study.Further-more,this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s,notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC. [Conclusions]The experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC.By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events,this approach can more effectively handle scenarios with restricted computational resources,ensuring superior control precision and stability.Furthermore,it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots,offering a more practical and reliable solution for their practical application.关键词
割草机器人/模型预测控制/路径跟踪/模糊控制/事件触发执行机制Key words
mowing robot/model predictive control/path tracking/fuzzy control/event-triggered mechanism分类
农业工程引用本文复制引用
贺庆,冀杰,冯伟,赵立军,张博涵..割草机器人自适应时域MPC路径跟踪控制方法[J].智慧农业（中英文）,2024,6(3):82-93,12.基金项目
重庆市研究生科研创新项目(CYS23207) （CYS23207）
重庆市科学技术局农业农村领域重点研发项目(cstc2021jscx-gksbX0003) （cstc2021jscx-gksbX0003）
重庆市教育委员会科学技术研究项目(KJZD-M202201302) （KJZD-M202201302）
重庆市科技局创新发展联合基金项目(CSTB2022NSCQ-LZX0024) Chongqing Graduate Research Innovation Project(CYS23207) （CSTB2022NSCQ-LZX0024）
Chongqing Science and Technology Bureau Agri-culture and Rural Key Research and Development Project(cstc2021jscx-gksbX0003) （cstc2021jscx-gksbX0003）
Science and Technology Research Project of Chongqing Education Commission(KJZD-M202201302) （KJZD-M202201302）
Chongqing Science and Technology Bureau Innovation Development Joint Fund Project(CSTB2022NSCQ-LZX0024) （CSTB2022NSCQ-LZX0024）
