PID controller for tracking of autonomous mobile robot combined with Artificial Potential field and Iterative learning control

A path tracking and obstacle avoidance system along with iterative control learning for an autonomous mobile robot is investigated. The location of static obstacles is known, and a path planning algorithm is implemented while considering the robot’s kinematic limitations (such as size, weight, and steering angles). The first contribution of this work is the implementation of an artificial potential field algorithm for trajectory generation while avoiding static obstacles. A multi-constrained Proportional-Integral-Derivative controller is used for trajectory tracking. The second contribution is the implementation of an iterative learning control for minimum time iterative control tasks. The system’s distance coordinates, and trajectory is stored for each lap and used to develop recursive feasibility and time efficient performance cost for each iteration. Simulation results show the effectiveness of the proposed control logic.