Iterative learning control yields accurate feedforward input by utilizing experimental data from past iterations. However, typically there exists a tradeoff between task flexibility and tracking performance. This study aims to develop a learning framework with both high task-flexibility and high tracking-performance by integrating rational basis functions with frequency-domain learning. Rational basis functions enable the learning of system zeros, enhancing system representation compared to polynomial basis functions. The developed framework is validated through a two-mass motion system, showing high tracking-performance with high task-flexibility, enhanced by the rational basis functions effectively learning the flexible dynamics.

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Iterative learning control (ILC) yields substantial performance improvement for repetitive motion tasks. While task-flexibility for non-repetitive motion tasks can be achieved with the use of basis functions, this typically comes with a trade-off in performance or design parameters. This study aims to achieve both task-flexibility and high performance with a single time-domain optimization framework. By defining a criterion combining the cost for performance and task-flexibility, an optimal feedforward with task-flexibility of basis function ILC and high performance surpassing standard norm-optimal ILC is obtained. Numerical validation on a two-mass motion system confirm the capabilities of the developed framework.

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BState estimation is essential for tracking conditions which can not be directly measured by sensors, or are too noisy. The aim of this poster is to present an approach to mitigate the phase delay without compromising the noise sensitivity, by using accessible future data. Such use of future data can be possible in cases like Iterative Learning Control, where full data of the previous trial is acquired beforehand. The effectiveness of the presented approach is verified through a motion system experiment, successfully showing the state estimation improvement in time domain. The presented non-causal approach improves the trade-offs between the phase delay of the estimation and the noise sensitivity of the state observer.@en

State-tracking Iterative Learning Control (ILC) yields perfect state-tracking performance at each n sample instances for systems that perform repetitive tasks, where n stands for the order of the system. By achieving perfect state-tracking, oscillatory intersample behavior often encountered in output-tracking ILC has been mitigated. However, state-tracking ILC only assures the estimated state error to converge to a significantly small value, meaning the accuracy of the state estimation takes a critical role. State estimation using a causal state observer has had an inevitable trade-off between the estimation delay and the noise sensitivity. By utilizing the non-causal operation of ILC, a non-causal state estimation can be designed. This non-causal state estimation performs beyond the trade-off of causal estimation, improving the estimation delay without compromising the noise sensitivity. The aim of this paper is to implement the non-causal state observer to state-tracking ILC, and present the improved state tracking by applying it to a second order system.

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