Iterative Learning Control for Friction Compensation of a Lorentz Actuator for Periodic References

This paper presents a robust control strategy integrating Iterative Learning Control (ILC) to improve the tracking performance of a linear Lorentz force actuator in presence of friction and model uncertainties. The approach leverages ILC to compensate for nonlinear friction effects for various periodic reference commands. The method adapts to time-varying friction by dynamically adjusting the ILC effort, reducing errors, and improving convergence rates using Kalman filter. Experimental results demonstrate the effectiveness of this method in achieving improved trajectory tracking across multiple tasks and varying reference commands.