割草机器人自适应时域MPC路径跟踪控制方法
[目的/意义]传统路径跟踪模型预测控制（Model Predictive Control，MPC）大多采用固定时域，较少考虑道路弯曲和曲率变化的影响，使得机器人在曲线路径作业过程中的跟踪效果和适应性都较差。因此，设计了一种自适应时域MPC控制器并使其满足自主割草等复杂作业要求。[方法]首先，根据割草机器人的速度确定前方参考路径的预瞄区域，并计算预瞄区域内的参考路径曲度因子和曲度变化因子，分别用于描述曲率和曲率变化大小。然后，将二者作为模糊控制器的输入信息，用于自适应调节MPC的预测时域，同时，根据预测时域及曲度变化因子调整控制时域，以增强控制器对路径弯曲变化的适应性并降低计算资源。此外，设计一...
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| Published in | 智慧农业（中英文） Vol. 6; no. 3; pp. 82 - 93 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国农业科学院农业信息研究所
30.05.2024
西南大学 工程技术学院,重庆 400715,中国%重庆市农业科学研究院 农业机械研究所,重庆 401329,中国%重庆文理学院 智能制造工程学院,重庆 402160,中国 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-8094 |
| DOI | 10.12133/j.smartag.SA202401010 |
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| Abstract | [目的/意义]传统路径跟踪模型预测控制（Model Predictive Control，MPC）大多采用固定时域，较少考虑道路弯曲和曲率变化的影响，使得机器人在曲线路径作业过程中的跟踪效果和适应性都较差。因此，设计了一种自适应时域MPC控制器并使其满足自主割草等复杂作业要求。[方法]首先，根据割草机器人的速度确定前方参考路径的预瞄区域，并计算预瞄区域内的参考路径曲度因子和曲度变化因子，分别用于描述曲率和曲率变化大小。然后，将二者作为模糊控制器的输入信息，用于自适应调节MPC的预测时域，同时，根据预测时域及曲度变化因子调整控制时域，以增强控制器对路径弯曲变化的适应性并降低计算资源。此外，设计一种MPC事件触发执行机制，进一步提升MPC的实时性。[结果和讨论]与固定时域的MPC进行对比试验，自适应时域MPC控制器的最大横向误差绝对值和最大航向误差绝对值分别控制在11 cm和0.13 rad以内，其平均求解时间比最大时域MPC减少10.9 ms。[结论]自适应时域MPC不仅能够保证割草机器人对曲线路径的跟踪精度，同时降低了MPC求解计算量并提高了控制实时性，解决了固定时域MPC的控制精度与计算量之间的矛盾。 |
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| AbstractList | S224.1+5%TP242.6; [目的/意义]传统路径跟踪模型预测控制(Model Predictive Control,MPC)大多采用固定时域,较少考虑道路弯曲和曲率变化的影响,使得机器人在曲线路径作业过程中的跟踪效果和适应性都较差.因此,设计了一种自适应时域MPC控制器并使其满足自主割草等复杂作业要求.[方法]首先,根据割草机器人的速度确定前方参考路径的预瞄区域,并计算预瞄区域内的参考路径曲度因子和曲度变化因子,分别用于描述曲率和曲率变化大小.然后,将二者作为模糊控制器的输入信息,用于自适应调节MPC的预测时域,同时,根据预测时域及曲度变化因子调整控制时域,以增强控制器对路径弯曲变化的适应性并降低计算资源.此外,设计一种MPC事件触发执行机制,进一步提升MPC的实时性.[结果和讨论]与固定时域的MPC进行对比试验,自适应时域MPC控制器的最大横向误差绝对值和最大航向误差绝对值分别控制在11 cm和0.13 rad以内,其平均求解时间比最大时域MPC减少10.9 ms.[结论]自适应时域MPC不仅能够保证割草机器人对曲线路径的跟踪精度,同时降低了MPC求解计算量并提高了控制实时性,解决了固定时域MPC的控制精度与计算量之间的矛盾. [目的/意义]传统路径跟踪模型预测控制（Model Predictive Control，MPC）大多采用固定时域，较少考虑道路弯曲和曲率变化的影响，使得机器人在曲线路径作业过程中的跟踪效果和适应性都较差。因此，设计了一种自适应时域MPC控制器并使其满足自主割草等复杂作业要求。[方法]首先，根据割草机器人的速度确定前方参考路径的预瞄区域，并计算预瞄区域内的参考路径曲度因子和曲度变化因子，分别用于描述曲率和曲率变化大小。然后，将二者作为模糊控制器的输入信息，用于自适应调节MPC的预测时域，同时，根据预测时域及曲度变化因子调整控制时域，以增强控制器对路径弯曲变化的适应性并降低计算资源。此外，设计一种MPC事件触发执行机制，进一步提升MPC的实时性。[结果和讨论]与固定时域的MPC进行对比试验，自适应时域MPC控制器的最大横向误差绝对值和最大航向误差绝对值分别控制在11 cm和0.13 rad以内，其平均求解时间比最大时域MPC减少10.9 ms。[结论]自适应时域MPC不仅能够保证割草机器人对曲线路径的跟踪精度，同时降低了MPC求解计算量并提高了控制实时性，解决了固定时域MPC的控制精度与计算量之间的矛盾。 |
| Abstract_FL | [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. |
| Author | 冀杰 冯伟 张博涵 贺庆 赵立军 |
| AuthorAffiliation | 西南大学 工程技术学院,重庆 400715,中国%重庆市农业科学研究院 农业机械研究所,重庆 401329,中国%重庆文理学院 智能制造工程学院,重庆 402160,中国 |
| AuthorAffiliation_xml | – name: 西南大学 工程技术学院,重庆 400715,中国%重庆市农业科学研究院 农业机械研究所,重庆 401329,中国%重庆文理学院 智能制造工程学院,重庆 402160,中国 |
| Author_FL | FENG Wei ZHANG Bohan HE Qing JI Jie ZHAO Lijun |
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| Keywords | 模型预测控制 割草机器人 路径跟踪 事件触发执行机制 模糊控制 model predictive control fuzzy control path tracking event-triggered mechanism mowing robot |
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| PublicationTitle | 智慧农业（中英文） |
| PublicationTitle_FL | Smart Agriculture |
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| Publisher | 中国农业科学院农业信息研究所 西南大学 工程技术学院,重庆 400715,中国%重庆市农业科学研究院 农业机械研究所,重庆 401329,中国%重庆文理学院 智能制造工程学院,重庆 402160,中国 |
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| Snippet | [目的/意义]传统路径跟踪模型预测控制（Model Predictive Control，MPC）大多采用固定时域，较少考虑道路弯曲和曲率变化的影响，使得机器人在曲线路径作业过程中的跟踪效... S224.1+5%TP242.6; [目的/意义]传统路径跟踪模型预测控制(Model Predictive Control,MPC)大多采用固定时域,较少考虑道路弯曲和曲率变化的影响,使得机器人在曲线路径作业过... |
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