基于MPC延时补偿器的农机多机器人编队行驶轨迹跟踪方法OACSTPCD
Trajectory Tracking Method of Agricultural Machinery Multi-Robot Formation Operation Based on MPC Delay Compensator
[目的/意义]农机装备尺寸大、行驶慢等特点,在路面归库作业过程中容易造成严重道路拥堵.因此,在多机协同过程中,编队行驶被认为是未来道路上行驶的主要方式.然而,目前农机自动驾驶技术停留在单机阶段,多农机之间的协同仍是制约中国农业规模化自主生产的主要瓶颈.为解决多车编队协同控制中通信延时的问题及其补偿策略,本研究基于一种模型预测控制器(Model Predictive Control,MPC)延时补偿器的农机多机编队行驶的轨迹跟踪方法.[方法]以车联网技术为基础,聚焦多农机编队协同控制领域,针对控制器局域网总线(Con-troller Area Network,CAN)通信存在的延时问题而产生的横向控制精度差,基于线性二次调节器(Linear Quadratic Regulator,LQR)与MPC算法,设计了一种带有延时补偿的模型预测控制器,用于对通信延时进行补偿控制.最后对所提算法利用Carsim和Simulink软件进行联合仿真.[结果和讨论]Carsim与MATLAB/Simulink可以有效兼容,实现软件与外部求解器的联合仿真.当延时步长d=5时,应用延时补偿,MPC反应速度更快,表现更为平滑;速度误差曲线响应更快,且能够逐渐稳定至零误差,没有出现振荡现象;1号车在较短时间内有效地变更车道,与头车保持在同一车道上.在更长的延时步长d=10情况下,未应用延时补偿的控制器表现出更显著的性能下降.即使在较高的延时条件下,应用延时补偿的MPC速度误差和纵向加速度仍然能够快速响应并逐渐稳定至零误差,避免了振荡现象.1号车的轨迹表明,延时补偿机制效果在极端延时条件下有所下降.[结论]本研究所设计的编队算法能够使得多车完成多车变道形成队列并保持一定距离和一定速度.通信延时补偿控制算法使得带有加入延时的车辆能较好完成编队任务,实现稳定的横纵向控制,验证了本研究带有延时补偿的模型预测控制器的可行性.
[Objective]The technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization.By organizing multiple agricultural machinery units into convoys,unified control and collaborative operations can be achieved.This not only enhances operational efficiency and reduces costs,but also minimizes human labor input,thereby maximizing the operational potential of agricultural machinery.In order to solve the problem of communication delay in cooperative control of multi-vehicle for-mation and its compensation strategy,the trajectory control method of multi-vehicle formation was proposed based on model predic-tive control(MPC)delay compensator. [Methods]The multi-vehicle formation cooperative control strategy was designed,which introduced the four-vehicle formation coop-erative scenario in three lanes,and then introduced the design of the multi-vehicle formation cooperative control architecture,which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model,and laied down a sturdy foundation for solving the formation following problem later.The testing and optimization of automatic driving algo-rithms based on real vehicles need to invest too much time and economic costs,and were subject to the difficulties of laws and regula-tions,scene reproduction and safety,etc.Simulation platform testing could effectively solve the above question.For the agricultural automatic driving multi-machine formation scenarios,the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines.Based on the single-machine dynamics model of the agricultural ma-chine,a delay compensation controller based on MPC was designed.Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization.Based on the above model,the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution.The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible.And because the operation range of the actuator was limited,the control increment and control volume were designed with corresponding constraints.Finally,the control increment constraints were solved based on the front wheel angle constraints,front wheel angle increments,and control volume constraints of the agricultural machine. [Results and Discussions]Carsim and MATLAB/Simulink could be effectively compatible,enabling joint simulation of software with external solvers.When the delay step size d=5 was applied with delay compensation,the MPC response was faster and smoother;the speed error curve responded faster and gradually stabilized to zero error without oscillations.Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle.In the case of a longer delay step size d=10,controllers without delay com-pensation showed more significant performance degradation.Even under higher delay conditions,MPC with delay compensation ap-plied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error,avoiding oscilla-tions.The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions.The simulation results validated the effectiveness of the proposed formation control algorithm,ensuring that multiple vehi-cles could successfully change lanes to form queues while maintaining specific distances and speeds.Furthermore,the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks,achieving stable lon-gitudinal and lateral control.This confirmed the feasibility of the model predictive controller with delay compensation proposed. [Conclusions]At present,most of the multi-machine formation coordination is based on simulation platform for verification,which has the advantages of safety,economy,speed and other aspects,however,there's still a certain gap between the idealized model in the sim-ulation platform and the real machine experiment.Therefore,multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations.
栾世杰;孙叶丰;贡亮;张凯
上海出版印刷高等专科学校,上海 200093,中国上海交通大学 机械与动力工程学院,上海 200240,中国南京信息工程大学 自动化学院,江苏南京 210044,中国
农业工程
车联网技术智能农机多机协同编队行驶轨迹跟踪
vehicle to everythingintelligent agricultural machinerymulti-machine collaborationtraveling in formationtrajectory tracking
《智慧农业(中英文)》 2024 (003)
69-81 / 13
上海市科技兴农项目(沪农科推字[2022]第3-1号);国家重点研发计划项目(2022YFD2001201) Shanghai Science and Technology Agriculture Project(Shanghai Nongke Tuzi[2022]No.3-1);Key R&D Project of Ministry of Science and Technology(2022YFD2001201)
评论