Iterative learning control for a class of systems with hysteresis

Hysteresis characteristics is highly nonlinear, has memory and is common in engineering systems. Its presence introduces uncertainties and nonlinearity in dynamic modelling and thus difficulties in achieving a good control design. This paper studies the suitability of iterative learning control (ILC) to compensate hysteresis uncertainties for a class of continuous-time dynamic systems. We examine dynamic systems with Preisach model hysteresis nonlinearity. It is shown that this class of systems possess properties of continuity and repeatability which are required for ILC. Furthermore, anticipatory iterative learning control (or A-type ILC) is applied to overcome the uncertainties and nonlinearity introduced by hysteresis. Simulation results are presented to validate the effectiveness of ILC laws to eliminate tracking error due to hysteresis uncertainties.