Discrete open-closed-loop PID-type iterative learning control for trajectory tracking of tracked mobile robots

Abstract

In this article, a robust discrete-time open-closed-loop proportion integral differential (PID) -type iteration learning control (ILC) algorithm is developed for the high-precision trajectory tracking control of tracked mobile robots (TMRs) with external disturbances and noises. The proposed ILC algorithm adopts the past, current, and predictive learning error items of the former and current iterations to correct the current control input variables, which finally converges to the desired trajectory through continuous iterative learning. The convergence characterization of the algorithm for TMRs under both external disturbances and noises is carried on rigorous mathematical proof. Numerical simulations and physical experiments are provided to verify the feasibility and effectiveness of the algorithm. The comparative results of two ILC algorithms indicate that the tracking performance of the proposed ILC algorithm is superior to the traditional PID-type ILC algorithm in terms of tracking accuracy and convergence rate.