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

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 of tracked mobile robots (TMRs) with external disturbances and noises. The proposed ILC adopts past, current, predictive error items former current iterations to correct input variables, which finally converges desired through continuous iterative learning. convergence characterization TMRs under both noises carried on rigorous mathematical proof. Numerical simulations physical experiments are provided verify feasibility effectiveness algorithm. comparative results two algorithms indicate that performance superior traditional PID-type in terms accuracy rate.