Iterative Learning Control For Autonomous Driving Vehicles

In this paper, iterative learning control (ILC) is proposed to deal with the problem of designing the most optimal control signal in autonomous cars that require tracking a fixed reference trajectory and to address repeated disturbances such as sloping roads. By exploiting data from the previous iterations, the learning control algorithm can obtain better tracking control performance for the next run, and hence outperforms conventional control approaches such as PID controller. The learning algorithms can also be used in combination with other traditional control techniques, for example, the conventional feedback control is designed in the first iteration, then the learning control is applied to improve performance in the subsequent iterations. Simulation results demonstrate the potential benefits of the proposed method.