A Time-Varying Q-Filter Design for Iterative Learning Control

Linear time-invariant (LTI) lowpass Q-filters are often employed in iterative learning control (ILC) algorithms to provide robustness to model uncertainty at the expense of learning bandwidth. Whenever high frequency control is necessary, such as motion commands with rapid changes, precision tracking requirements may not be met. In this work, we examine the use of linear time-varying (LTV) Q-filters to create a time-varying learning bandwidth. Single-input single- output LTI plants with repeating disturbances are considered. Stability analysis for the LTV Q-filter learning algorithm is developed and an LTV Q-filter design procedure is presented incorporating time-frequency analysis of the tracking error and optimization. This procedure is used to design an LTV Q- filter for a microscale robotic deposition manufacturing system. Simulation and experimental results are provided, which demonstrate that the designed LTV Q-filter results in faster convergence and lower converged error than the best LTI Q-filter.