Fault-tolerant iterative learning control for mobile robots non-repetitive trajectory tracking with output constraints - Jin, Xu (Automatica (Oxford), 2018)
In this brief, we develop a novel iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of unicycle-type mobile robots with two actuated wheels that are subject to actuator faults. Unlike most of the ILC literature that requires identical reference trajectories over the iteration domain, the desired trajectories in this work can be iteration dependent, and the initial position of the robot in each iteration can also be random. The mass and inertia property of the robot and wheels can be unknown and iteration dependent. Barrier Lyapunov functions are used in the analysis to guarantee satisfaction of constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive constant and zero, respectively, over the iteration domain, beyond a small initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.