Multivariable parametric models are essential for optimizing the performance of high-tech systems. The main objective of this paper is to develop an identification strategy that provides accurate parametric models for complex multivariable systems. To achieve this, an additive model structure is adopted, offering advantages over traditional black-box model structures when considering physical systems. The introduced method minimizes a weighted least-squares criterion and uses a refined instrumental variable method to solve the estimation problem, achieving local optimality upon convergence. Experimental validation is conducted on a prototype wafer-stage system, featuring a large number of spatially distributed actuators and sensors and exhibiting complex flexible dynamic behavior, to demonstrate the effectiveness of the proposed method.

Data-driven estimation of system norms is essential for analyzing, verifying, and designing control systems. Existing data-based methods often do not capture the inter-grid and transient behavior of the system, leading to inaccurate and unreliable system norm estimations. This paper presents a unified approach for accurate and reliable estimation of the H₂ and H_∞ norm with a limited amount of data. The key step is to exploit local parametric models that explicitly incorporate the inter-grid and transient dynamics. The system norm is estimated through the computation of local system norms of the local parametric models within their the local frequency interval. Simulation and experimental results illustrate the effectiveness of the proposed method.

Increasingly stringent performance requirements for motion systems necessitate explicit control of the flexible dynamic behavior. The aim of this paper is to present an approach to identify spatio-temporal models of overactuated mechatronic systems with a limited number of spatially distributed sensors. The proposed approach exploits the modal modeling framework and exploits the symmetry in modal models to enhance the spatial resolution of the identified spatially-sampled modal models. Spatio-temporal models are identified by updating prior finite element method-based models based on the identified extended modal models. The experimental results illustrate the effectiveness of the proposed approach for the identification of complex position-dependent mechanical systems.

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.

Next-generation deformable mirrors are envisaged to exhibit low-frequency flexible dynamics and to contain a large number of spatially distributed actuators due to increasingly stringent performance requirements. The increasingly complex system characteristics necessitate identifying the flexible dynamic behavior for design validation and next-generation control. The aim of this paper is to develop a unified approach for the identification of mechanical systems with a large number of spatially distributed actuators and a limited number of sensors. A frequency domain-based approach using local modeling techniques is developed. The modal modeling framework is employed to analyze the design and create outputs that were not measured. The proposed approach is applied to an experimental deformable mirror case study that illustrates the effectiveness of the proposed approach.

Cross-coupled iterative learning control (ILC) can improve the contour tracking performance of manufacturing systems significantly. This paper aims to develop a framework for norm-optimal cross-coupled ILC that enables intuitive tuning of time- and iteration-varying weights of the exact contour error and its tangential counterpart. This leads to an iteration-varying ILC algorithm for which convergence conditions are developed. In addition, a resource-efficient implementation is developed that reduces the computational load significantly and enables the use of long reference signals. The approach is experimentally validated on an industrial flatbed printer.

The selection of uncertainty structures is an important aspect of system identification for robust control. The aim of this paper is to provide insight into uncertain multivariable systems for robust control. A unified method for visualizing model sets is developed by generating Bode plots of multivariable uncertain systems, both in magnitude and phase. In addition, these model sets are compared from the viewpoint of the control objective, allowing a quantitative analysis as well. An experimental case study on an automotive transmission application demonstrates these connections and confirms the importance of the developed framework for control applications. In addition, the experimental results provide new insights into the shape of associated model sets by using the presented visualization procedure. Both the theoretical and experimental results confirm that a recently developed robust-control-relevant uncertainty structure outperforms general dual-Youla-Kučera uncertainty, which in turn outperforms traditional uncertainty structures, including additive uncertainty.

Flexible dynamics in motion systems lead to inherent spatio-temporal system behavior. The aim of this paper is to develop an unified approach for the identification of modal models of spatio-temporal overactuated systems. The approach exploits the modal modeling framework and the overactuated setting to enhance the estimation of the spatial system behavior. The proposed approach is applied in an experimental case study. The case study considers an experimental overactuated stage and illustrates the effectiveness of the proposed approach.

Piezo stepper actuators are very promising for nanopositioning systems due to their high resolution, high stiffness, fast response, and the ability to position a mover over an infinite stroke by means of motion reminiscent of walking. The aim of this paper is to enhance the waveforms for actuating piezo steppers, by actively compensating for repetitive disturbances that are introduced by the walking behavior. A compensation method is developed to compensate for disturbances in the stepping domain, since disturbances may vary in the time domain if the velocity changes. The results from this procedure are exploited to determine an optimal waveform for the working range of the actuator. A significant improvement in performance is achieved after applying this waveform to a piezo stepper actuator.

Next-generation motion stages are envisaged to be lightweight to meet stringent demands regarding accuracy and throughput. The lightweight stage design implies flexible dynamical behavior, which is foreseen to be severely excited due to increasing acceleration forces. The aim of this paper is to exploit additional actuators and sensors to explicitly control the flexible dynamic behavior. A systematic weighting filter design procedure is introduced which is tailored to next-generation motion stages. The presented procedure naturally connects to existing robust control techniques. The procedure is applied to an experimental next-generation reticle stage confirming performance enhancement by exploiting additional actuators and sensors beyond traditional performance limitations.