A Norm Optimal Approach to Time-Varying ILC With Application to a Multi-Axis Robotic Testbed

In this paper, we focus on improving performance and robustness in precision motion control (PMC) of multi-axis systems through the use of iterative learning control (ILC). A norm optimal ILC framework is used to design optimal learning filters based on design objectives. This paper contains two key contributions. The first half of this paper presents the norm optimal framework, including the introduction of an additional degree of design flexibility via time-varying weighting matrices. This addition enables the controller to take trajectory, position-dependent dynamics, and time-varying stochastic disturbances into consideration when designing the optimal learning controller. Explicit guidelines and analysis requirements for weighting matrix design are provided. The second half of this paper seeks to demonstrate the use of these guidelines. Using the design details provided in the paper, norm optimal learning controllers using time-invariant and time-varying weighting matrices are designed for comparison through simulation on a model of a multi-axis robotic testbed.