基于ILA一LQR一PID的田间智能作业车路径跟踪控制

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中图分类号：S126；TP242.6 文献标识码：A 文章编号：2095-5553（2026）02-0157-12

Abstract：Wheninteligentvehiclesoperatein fields，theyoften experience slippage duetolowfrictioncoeficients causedbyslipperygroundconditions.Inordertoimprovetheeficiencyof intellgentvehicleoperationsinthefield， reduceslippageonwet surfaces，andaddress thechalengeof determining controlalgorithmparameters，this study proposesa control method called Incomprehensible but Intelligible in time logics （ILA）for determining parameters of an LQR—PID controller，termedILA—LQR—PID，which employs a strategyforpath trackingcontrol.Firstly，the dynamicmodelof theintellgentvehicleisestablished．Then，feedforwardLQRlateral trackingcontroller，PID longitudinalvelocitytrackingcontroller，steering slipcontroler，andbrakingslipcontrolleraredesigned.Subsequently， thecore parametersofeachcontroleraredetermined byusing thelogicaloptimizationalgorithm ILA.Tovalidate the feasibiltyof the strategy，joint simulation experimentsare conducted byusing MATLAB/Simulink and Carsim.The results show that under the conditions of a vehicle speed of 10km/h and a friction coefficient of O.9，ILA—LQR—PID algorithm strategycompares thecontrol methodCOA—LQR—PID for determining controler parameterswith the original LQR—PID and the Coati Optimization Algorithm（COA）method，the average speed error，maximum lateral error and arrival error in different environments are reduced by 88.1% ， 85.8% ， 98.6% ，and 6% ， 41.1% ， 57.3% ， respectively.Under the conditions of a vehicle speed of 10km/h and a friction coefficient of O.3，they are reduced by 61.1% ， 76.9% ， 97.7% ，and 28.5% ， 36.3% ， 58.2% ，respectively. Under conditions of a vehicle speed of 30km/h （202 anda friction coefficient of O.9，they are reduced by 44.8% ， 92.2% ， 99.1% ，and 26.9% ， 77.8% ， 91.5% ， respectively.Moreover，theerrorsareall inthecentimeterrange，metingtherequirementsoffieldoperations，and providing technical support for smart agriculture.

Keywords：field inteligentoperationvehicle;path tracking;LQRcontrol;PIDcontrol;logical optimizationalgorithms

0 引言

传统的农业作业方式大多依赖于人工手动播种、收割、喷药以及测量等，其智能化和精细化程度都比较低，劳动密集消耗了大量的劳动力，生产效率较低[1]。（剩余12732字）