Increasing performance requirements in high-precision mechatronic systems lead to a situation where both multivariable and sampled-data implementation aspects need to be addressed. The aim of this paper is to develop a design framework for a multi-input multi-output feedforward controller to improve continuous-time tracking performance through learning. The sampled-data feedforward controller is designed with physically interpretable tuning parameters using a multirate zero-order-hold differentiator. The developed approach enables interaction compensation for multi-input multi-output systems and the feedforward controller parameters are updated through learning. The performance improvement is experimentally validated in a multi-input multi-output motion system compared to the conventional feedforward controllers.@en

The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new criterion that emphasizes the closed-loop control objective, inclusion of preview to deal with delays and non-minimum phase dynamics, and enabling the use of an iterative learning algorithm to generate training data in view of addressing generalization errors. The approach is illustrated through simulations and experiments on an industrial flatbed printer.

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Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to achieve both the task flexibility enabled by ILC with basis functions and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.

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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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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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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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Sampled-data control requires both on-sample and intersample performance in high-precision mechatronic systems. The aim is to design a discrete-time linearly parameterized feedforward controller to improve both on-sample and intersample performance in a multi-modal motion system. The continuous-time performance is considered as state compatibility by a multirate zero-order-hold differentiator. The developed approach enables the linearly parameterized feedforward controller design for sampled-data systems with physically intuitive tuning parameters. The performance improvement is validated by comparing the developed approach with a conventional approach using a backward differentiator for a multi-modal motion system.

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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

Feedforward control has an important role in high-precision mechatronic systems. The aim of this research is to design a discrete-time feedforward controller to improve on-sample and intersample errors. The developed approach is parameterized using a linear combination of parameters and basis functions, which results in a parameterization that has intuitive physical meaning. The basis functions are designed with a differentiator that considers the sampled-data and zero-order-hold aspects. The performance improvement is demonstrated by comparing the developed approach with a conventional basis function design for a motion system.@en

Iterative learning control (ILC) yields perfect output-tracking performance at sampling instances for systems that perform repetitive tasks. The aim of this article is to develop a framework for a state-tracking ILC that mitigates oscillatory intersample behavior, which is often encountered in output tracking ILC. As a framework for the analysis, the stability of the iterative domain including the robustness filter and the asymptotic signal is formulated. In addition, as a framework for the design, the design method using frequency response data to reduce the modeling effort, the learning filter design based on inversion, and the specific design procedure of the robustness filter are presented. The designed method is successfully applied to a motion system and it is shown that the presented state-tracking ILC provides better intersample behavior than the standard output-tracking ILC.

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