Noncausal State Estimation for Iterative Learning Control
Kentaro Tsurumoto (University of Tokyo)
Wataru Ohnishi (University of Tokyo)
Takafumi Koseki (University of Tokyo)
Nard Strijbosch (Eindhoven University of Technology)
Tom Oomen (TU Delft - Mechanical Engineering, Eindhoven University of Technology)
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Abstract
Next-generation high-precision mechatronic systems require safe and precise control of unmeasurable states. State-tracking iterative learning control (ILC) can achieve extremely high state-tracking performance up to the performance of state estimation, with convergence guaranteed apriori through the frequency-domain characteristics of the state estimator. The aim of this study is to develop a noncausal state estimation framework with verifiable frequency-domain characteristics. In batch-operated systems such as ILC, the use of noncausal design leads to substantial performance improvements that surpass the fundamental limits of causal approaches. Furthermore, by analytically verifying the frequency-domain characteristics of the noncausal state estimator, the developed framework retains the benefit of guaranteeing convergence in ILC. The developed framework is validated both by simulation and experiment, confirming improved state-tracking with monotonic convergence of ILC, achieved by exploiting noncausality in state estimation.