Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer

Selecting uncertainty structures in identification for robust control with an automotive application

The selection of uncertainty structures is an important aspect in system identification for robust control. The aim of this paper is to investigate the consequences for multivariable systems. Hereto, first a theoretical analysis is performed that establishes the connection between the associated model set and the robust control criterion. Second, an experimental case study for an automotive application confirms these connections. In addition, the experimental results provide new insights in the shape of associated model sets by using a novel validation procedure. Finally, the improved connections are confirmed through a robust controller synthesis. Both the theoretical and experimental results confirm that a recently developed robust-control-relevant uncertainty structure outperforms general dual-Youla-Kucera uncertainty, which in turn outperforms traditional uncertainty structures, including additive uncertainty

A robust-control-relevant perspective on model order selection

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Aliasing of Resonance Phenomena in Sampled-Data Feedback Control Design: Hazards, Modeling, and a Solution

High-performance control design for electromechanical sampled-data systems with aliased plant dynamics is investigated. Though from a theoretical viewpoint the aliasing phenomenon is automatically handled by direct sampled-data control, such an approach cannot be used in conjunction with models derived through system identification. From a practical viewpoint, aliasing is often considered as an undesirable phenomenon and a typical remedy is the increase of the sampling frequency. However, the sampling frequency is upper bounded due to physical and economical constraints and aliasing may be inevitable. Control design for plants with aliased dynamics has not received explicit attention in the literature and it is not clear how to handle this situation. In this paper, it is shown that aliased resonance phenomena can effectively be suppressed in sampled-data feedback control design without the need for increasing the sampling frequency. Furthermore, it is shown experimentally on an industrial wafer stage that ignoring aliasing during control design can have a disastrous effect on closed-loop performance. Additionally, a novel, practically feasible procedure for identification of (possibly aliased) resonance phenomena based on multirate system theory is proposed

Iterative feedforward control with application to a wafer stage

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Batch-to-batch rational feedforward tuning : from learning to identification approaches

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Intelligente regeltechniek voor nieuwe generatie mechatronica

Intelligentere aansturing is essentieel om toekomstige mechatronische systemen aan de steeds strenger wordende nauwkeurigheids- en snelheidseisen te laten voldoen. Recente doorbraken op regeltechnisch gebied door intensieve samenwerking tussen academia en research- en developmentafdelingen maken het mogelijk om aan deze toekomstige producteisen te voldoen. De recente installatie van een hightech flatbed printer van Océ bij de Control Systems Technology groep en de oprichting van het High Tech Systems Center moeten de ontwikkeling van een systematische en industrieel toepasbare aanpak voor intelligente aansturing voor mechatronische systemen mogelijk maken