Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.

Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first estimated using a refined instrumental variable method and subsequently projected onto a modal form. The developed identification method provides accurate, physically-relevant, minimal-order models, for both generally-damped and proportionally damped modal systems. The effectiveness of the proposed method is demonstrated through experimental validation on a prototype wafer-stage system, which features a large number of spatially distributed actuators and sensors and exhibits complex flexible dynamics.

Noise from auxiliary subsystems, amplified by their own control system, can couple to the output signal of gravitational wave detectors, limiting the maximum attainable sensitivity. Subtraction filters can be used to mitigate this coupling of noise by adding a secondary disturbance path with the purpose of canceling the noise in the output of the detector. The aim of this paper is to develop a systematic approach for the design and online adaptive estimation of subtraction filters. The proposed method adaptively updates the subtraction filter without the need for external perturbations to the system, providing a robust approach towards handling the time-varying couplings in the system as well as reducing the need for detector downtime. The method is validated on a representative simulation of the Advanced Virgo+ gravitational wave detector, illustrating that the method is capable of suppressing the coupling of noise from an auxiliary subsystem while the coupling varies over time.

Mechanical ventilators are essential for patients who are unable to breathe independently. The aim of this article is to develop a systematic control design methodology that achieves accurate tracking of both the pressure and flow to ensure comfortable breathing for the patient. A hybrid controller is introduced that ensures improved baseflow tracking performance. The actual controller design leverages frequency-based techniques and is based on static decoupling and the factorized Nyquist criterion. Furthermore, a theoretical stability analysis of the hybrid controller is presented. The presented control strategy is implemented in a real ventilator, and it is demonstrated that the tracking performance is improved by conducting an experimental case study.

Feedforward motion control for unmeasured performance variables at the point of interest is crucial for attaining high throughput and accuracy in motion systems. The aim of this paper is to develop a data-driven approach for feedforward tuning that addresses the true performance at the point of interest. The presented approach is a novel methodology that employs rational feedforward structures for performing flexible tasks with high accuracy, in conjunction with an sensor fusion for addressing the point-of-interest. In particular, the tracking error of the unmeasured performance variable is accurately estimated by combining acceleration measurements and encoder measurements. Simulation results show that optimizing for the estimated point-of-interest error achieves similar tracking performance as optimizing for the true point-of-interest error, indicating accurate sensor-fusion estimates for feedforward control. Experimental validation demonstrates that optimizing for the estimated point-of-interest error significantly reduces the estimated point-of-interest tracking error compared to minimizing the encoder error.

Free-space optical satellite communication terminals rely on accurate metrology of their pointing mirrors to correctly aim their laser to a counter terminal, while at the same time requiring simple, lightweight and low-cost sensors. The aim of this paper is to develop an automated procedure for the calibration of these sensors in a mass production setting, using a pointing test bench (PTB) to automatically calibrate the angular sensors of many Coarse Pointing Assemblies (CPAs), which position pointing mirrors over a large field-of-regard. The PTB and CPA are aligned using feedback over an external optical position sensor (OPS) and an inverse kinematic model is learned from data, after which Gaussian Process regression models are created to predict and correct sensor errors, taking into account propagation of calibration errors from the PTB to the CPA. Experimental results show that the CPA sensor errors are reduced by two orders of magnitude by this automated calibration approach, even at orientations at which the PTB itself is uncalibrated. The developed framework is generalizable to calibration of arbitrary 2 degree of freedom (2-DOF) rotary systems and is not limited to specific types of position sensors, thereby enabling significant cost savings and increased accuracy in mass production of satellite communication terminals.

Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances. This paper develops a novel direct data-based ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level. Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems’ Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.

Many industrial motion systems require performing a variety of tasks with high precision and safety. Iterative learning control (ILC) is a method with convergent update laws, generally classified into: 1) parametrized learning approach for achieving task-flexibility against varying tasks; or 2) signal-based learning approach which can achieve perfect tracking-performance for repeating tasks. The aim of this study is to join the distinct ILC frameworks, achieving all desirable properties in a single framework. Specifications on convergence, tracking-performance and task-flexibility of the developed joint parametrized/signal-based ILC are theoretically derived, confirmed with experimental results on a two-mass system.

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.

Frequency Response Function (FRF) measurements are widely used in gravitational wave detectors, e.g., for the design of controllers, calibrating signals, and diagnosing problems with system dynamics. The aim of this paper is to present GraFIT: a toolbox that enables fast, inexpensive, and accurate identification of FRF measurements for gravitational wave detectors compared to the commonly used approaches, including common spectral analysis techniques. The toolbox builds upon recent developments in non-parametric system identification by utilizing a local modeling technique, which is particularly effective in reducing the impact of transient effects. It is furthermore designed to be user-friendly, handling systems of arbitrary input-output dimensions, and systems operating in a closed loop. The toolbox is validated on two experimental case studies of the Virgo detector, illustrating more than a factor 3 reduction in the estimated standard deviation using GraFIT for the same measurement times and comparable estimated standard deviations with up to ten times less data using GraFIT with respect to the commonly used spectral analysis method. As a result, GraFIT can reduce commissioning time and detector downtime due to noise injections, while also improving the overall quality of the measurements.

The state and output estimation accuracy depends on both the observer and the sensor locations. This paper focuses on this co-design problem in lithography applications. A theoretical formulation of this co-design problem is presented and solved for discrete-time linear stochastic models. We compare the optimal solution of two variants of the estimation problem. The first variant minimizes the transient estimation error whereas the second one minimizes the steady-state estimation error. In both cases, the Kalman filter is optimal. While solving these problems for a lithography application formulated as a 3D thermoelastic model, we observe a significant difference between the optimal sensor placements. Our results highlight the importance of designing a sensor layout in line with the desired transient or steady-state estimation performance.

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.

The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paper is to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function estimation in conjunction with iterative learning to directly learn parameter-varying feedforward control from data. This approach enables high tracking performance for feedforward control of linear parameter-varying dynamics, providing flexibility to varying reference tasks. The developed framework is validated on a benchmark industrial experimental setup featuring a belt-driven carriage.

Accurately modeling nonlinearities is becoming increasingly important for mechanical systems, particularly in the context of system design, model-based control and monitoring systems for fault diagnosis. In the nonlinear modeling process, a pivotal phase involves pinpointing the physical locations and quantifying the magnitude of nonlinearities. This paper introduces a data-driven approach for nonlinearity location and quantification by analyzing nonparametric frequency response functions. To achieve this objective, measurement locations in mechanical systems are interpreted as nodes arranged in a dynamic network, and linearization techniques are employed on the frequency response functions formed from node to node. The efficacy of the proposed approach and the concept of nonlinearity localization and quantification are illustrated by numerical simulations and experiments on a flexible beam setup.