Disturbances in iterative learning control (ILC) may be amplified if these vary from one iteration to the next, and reducing this amplification typically reduces the convergence speed. The aim of this paper is to resolve this trade-off and achieve fast convergence, robustness and small converged errors in ILC. A nonlinear learning approach is presented that uses the difference in amplitude characteristics of repeating and varying disturbances to adapt the learning gain. Monotonic convergence of the nonlinear ILC algorithm is established, resulting in a systematic design procedure. Application of the proposed algorithm demonstrates both fast convergence and small errors.

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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.

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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

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

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When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system. Traditional linear system identification may not consider the most parsimonious model when relying solely on unfactored transfer functions, which typically result from standard direct approaches. This paper presents a novel identification method that delivers additive models for both open and closed-loop setups. The estimators that are derived are shown to be generically consistent, and can admit the identification of marginally stable additive systems. Numerical simulations show the efficacy of the proposed approach, and its performance in identifying a modal representation of a flexible beam is verified using experimental data.

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This paper presents a framework for the derivation of a noise budget and the subsequent utilization in the optimization of the control design, using the laser frequency stabilization loop in the Virgo interferometer, which is a complex nested feedback system, as an experimental case study. First, the system dynamics and noise sources are modeled and experimentally verified to produce the noise budget, after which an optimization problem using the H2 norm is formulated and tailored to the specific design requirements for the detector. The structure of the synthesized controller is then used to produce an improved control design. Experimental verification of the developed controller on the Virgo interferometer shows roughly a factor 3 reduction in root-mean-square error, illustrating the effectiveness of the presented method.@en

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.

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Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various optimization contexts, the statistical properties of block coordinate descent in continuous-time system identification have not been covered in the literature. The aim of this letter is to formally analyze the bias properties of the block coordinate descent approach for the identification of MISO and additive SISO systems. We characterize the asymptotic bias at each iteration, and provide sufficient conditions for the consistency of the estimator for each identification setting. The theoretical results are supported by simulation examples.

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Iterative learning control (ILC) and repetitive control (RC) can lead to high performance by attenuating repeating disturbances perfectly, yet these approaches may amplify non-repeating disturbances. The aim of this paper is to achieve both perfect, fast attenuation of repeating disturbances and limited amplification of non-repeating disturbances. This is achieved by including a deadzone nonlinearity in the learning filter, which distinguishes disturbances based on their different amplitudes to apply different learning gains. Convergence conditions for nonlinear ILC and RC are developed, which are used in combination with system measurements in a comprehensive design procedure. Experimental implementation demonstrates fast learning and small errors.

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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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Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a parameter-varying mass-damper-spring system.

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Identifying structured discrete-time linear time/parameter-varying (LPV) input-output (IO) models with global stability guarantees is a challenging problem since stability for such models is only implicitly defined through the solution of matrix inequalities (MI) in terms of the model's coefficient functions. In this letter, a structured linear IO model class is developed that results in a quadratically stable model for any choice of coefficient functions, enabling identification using standard optimization routines while guaranteeing stability. This is achieved through transforming the MI-based stability constraints in a necessary and sufficient manner, such that for any choice of transformed coefficient functions the MIs are satisfied. The developed stable LPV-IO model is employed in simulation to estimate the parameter-varying damping of mass-damper-spring system with stability guarantees, while a standard LPV-IO model results in an unstable estimate.

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Feedforward control with task flexibility for MIMO systems is essential to meet the growing demands on throughput and accuracy of high-tech systems. The aim of this paper is to develop an experimentally efficient framework for data-driven tuning of rational feedforward controllers for general non-commutative MIMO systems. In the developed approach, the nonlinear terms in the non-convex optimisation problem of iterative learning control (ILC) are iteratively circumvented via approximation. This leads to a series of convex optimisation problems that can be solved offline to obtain the parameters of the rational feedforward controller. In addition, by limiting the number of offline iterations an experimentally intensive algorithm is derived, which could be beneficial in the case of severe model mismatch. A simulation study shows that the experimentally efficient approach converges fast through offline iterations and has improved convergence properties through the use of regularisation.

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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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A direct data-driven iterative algorithm is developed to accurately estimate the H_∞ norm of a linear time-invariant system from continuous operation, i.e., without resetting the system. The main technical step involves a reversed-circulant matrix that can be evaluated in a model-free setting by performing experiments on the real system.

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Estimation of the breathing effort and relevant lung parameters of a ventilated patient is essential to keep track of a patient's clinical condition. The aim of this paper is to increase estimation accuracy through experiment design. The main method is an experiment design approach across multiple breaths within a linear regression framework to accurately identify the patient's condition. Identifiability and persistence of excitation are used to formulate an estimation problem with a unique solution. Furthermore, Fisher information is used for assessing the parameters sensitivity to slight changes of the ventilator settings to improve the variance of the estimation. The estimation method is applied to simulated patients who breathe regularly but also to patients who have variable breathing patterns. A virtual experiment is conducted for both situations to generate estimation results. The results are analyzed using mathematical tools and show that uniquely estimating the lung parameters and breathing effort over multiple breaths for both regularly and variably breathing patients is possible in the presented framework. The proposed estimation method obtains clinically relevant estimates for a large set of breathing disturbances from the simulation case-study.

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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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Gravitational Wave detectors require low-noise sensors combined with high-performance feedback loops to maximize the detector sensitivity in the low-frequency detection range. Some feedback loops in the detector are strongly coupled and their coupling varies over time, which is inherent to the optical configuration and the optical readout scheme used to obtain error signals for the feedback loops. This paper presents a control-based approach to deal with the time-varying interaction among three of the longitudinal degrees of freedom in the Advanced Virgo Plus detector, using the pre-existing infrastructure of the detector. An intuitive Multiple-Input Multiple-Output stability criterion is presented that illustrates how the time-varying coupling affects the stability of the feedback loops, as well as a method that identifies the coupling online and updates a decoupling matrix accordingly. Experiment results of the proposed method on the Advanced Virgo Plus detector illustrate continuous minimization of the coupling over time. Using the derived stability criterion, it is furthermore shown that the coupling is sufficiently minimized such that the interaction terms can be neglected in the control design, resulting in an improved controller performance.@en

Model-free iterative learning control (ILC) can lead to high performance by attenuating repeating disturbances completely, using dedicated experiments on the real system to replace the traditional model. The aim of this paper is to develop a fast data-driven method for MIMO ILC that uses random learning in the form of efficient unbiased gradient estimates. This is achieved by developing a stochastic conjugate gradient algorithm, in which the search direction and optimal step size are generated using dedicated experiments. The approach is applied to MIMO automated feedforward tuning. Simulation and experimental results show that the method is superior to earlier stochastic and deterministic methods.

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Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.

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