Norm optimal ILC with time-varying weighting matrices

In this paper, we focus on improving performance and robustness in precision motion control (PMC) of multi-axis systems through the use of time-varying weighting matrices. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. framework is reformatted to include time-varying weighting matrices which enable the controller to take the trajectory, position-dependent dynamics, and time-varying disturbances into consideration when designing the optimal learning controller. A general approach for designing the different weighting matrices is included. The time-varying weighting approach of this framework enables one to focus on individual components that affect the system at different times throughout the trajectory independently. The performance benefits of timevarying weighting matrices are illustrated through simulation and experimental testing on a multi-axis robotic testbed.