Adaptive Lateral Control of Autonomous Trucks

Sammanfattning

Autonomous heavy vehicle control systems depend on a suite of parameters, bothvehicular and environmental, and their performance is directly related to these parametervalues. These parameters often include variables such as the total massof the loaded vehicle, lateral and longitudinal tire cornering coefficients, wheelbasegeometry, load distribution, etc. In theory, these parameters are often consideredconstant and known. However, in practice, this might not always be the case, especiallyin dynamic operating environments of a quarry or mine. Therefore, to ensurerobust and reliable performance, the controller must be adaptive, updating itselfonline to continue operating efficiently.This thesis focuses on the investigation, development, and implementation of differenttypes of adaptive lateral controllers for autonomous haulage trucks, whoseparameters initially deviate from the true value and are subject to change duringoperation. The study focuses on evaluating both direct and indirect approaches toadaptive control. The direct adaptive controller uses a Lyapunov-based approachtowards adaptation, meanwhile the indirect controller uses Kalman filter-based estimationmethods to estimate unknown system parameters.Overall, the findings suggest that the indirect adaptive control strategies are moresuitable for autonomous haulage applications. The integration of Kalman filterbasedparameter estimation with model based controllers like LQR resulted in consistentperformance and robustness, and better handling of system uncertaintiescompared to direct adaptive approaches.