Path Tracking Control of Autonomous Vehicles via Adaptive Iterative Learning Control

In this work, path tracking control of autonomous vehicles (AVs) are studied under the framework of adaptive iterative learning control (AILC). In order to facilitate the controller design, the nonlinear vehicle dynamics are firstly transformed into a parametric form by utilizing proper variable transformations. Then a robust adaptive learning control approach is developed to achieve the path tracking tasks. Furthermore, to deal with the actuator constraints, an input-dependent auxiliary system is employed to work together with the proposed learning controller, which thus helps to reduce their influence to the control performance. The convergence analysis for the proposed method is also provided by virtue of the Lyapunov-like theory. It has been shown that with the proposed AILC approach, a good path tracking performance can be ensured in the presence of system uncertainties, exteranl disturbances and input constraints. A numerical example is illustrated to verify its effectiveness.