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.

The kernel-based inverse system identification framework enables accurate identification of systems with non-minimum phase dynamics, greatly expanding the potential of non-causal system identification approaches. The existing kernel-based inverse system identification method performs the estimation assuming noisy input data, while in practice noise is typically present only in the output measurements. To address this impracticality, we propose a Bayesian identification method that employs the Expectation-Maximization algorithm and the Markov chain Monte Carlo method to enable direct identification of the inverse system using the available data. Through numerical simulations, we found that the proposed method allows for an accurate estimation of inverse models, and outperforms an indirect approach in both model fit and variance. The proposed method can be used to develop enhanced data-driven feedforward control methods that allow for flexible design while incorporating design specifications.

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.

Iterative learning control (ILC) is typically applied in practice combined with a feedback controller for time-domain stability. In this closed-loop design with actuator constraints, existing constrained ILC designs suffer from determining the exact input constraint on the ILC controller. This issue brings in an important gap between the existing constrained ILC designs and their real-world applications. This paper gives a systematic consideration of the input constraint problem in the closed-loop ILC design with actuator saturation. A constraint-aware ILC is developed to autonomously determine the constraint on the feedforward controller. The convergence of the constrained ILC process is proved under the framework of alternating projection. Finally, the effectiveness of the developed method is verified on a numerical simulation.

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate output measurements and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.

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 design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.

Safe deployment of neural networks to classify time series in safety-critical applications relies on the ability of the classifier to detect data that does not originate from the same distribution as the training data. The aim of this paper is to propose a framework for detecting whether time series data is sampled from a different distribution than the training data, known as the problem of out-of-distribution (OOD) detection. We propose a novel distance-based OOD method for time series data using a hierarchical clustering method together with dynamic time-warping to measure the difference between a new data instance and the training set. The method is evaluated in the context of mechanical ventilation, a safety critical application, using both simulated and clinical datasets. Results of the mechanical ventilation use case demonstrate that the proposed approach effectively detects out-of-distribution data and improves classification performance in diverse settings.

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.

Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this letter is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, providing a parsimonious and interpretable representation of many physical systems, and applies a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of multivariable parametric additive models in continuous time and is applicable to both open- and closed-loop systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system.

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.

Frequency-domain performance analysis of intersample behavior in sampled-data and multirate systems is challenging due to the lack of a frequency-separation principle, and systematic identification techniques are lacking. The aim of this paper is to develop an efficient technique for identifying the full intersample performance in the frequency-domain for closed-loop multirate systems, in particular the Performance Frequency Gain (PFG). Through local modeling techniques, aliased frequency components are effectively disentangled when identifying the PFG, which is directly facilitated by frequency-lifting the multirate system to a multivariable time-invariant representation. The developed method accurately and directly identifies the PFG in a single identification experiment. Finally, the developed method is experimentally validated on a prototype motion system, showing accurate identification of frequency-domain representations for the multirate system, including the PFG.

Online system identification algorithms are widely used for monitoring, diagnostics and control by continuously adapting to time-varying dynamics. Typically, these algorithms consider a model structure that lacks parsimony and offers limited physical interpretability. The objective of this paper is to develop a real-time parameter estimation algorithm aimed at identifying time-varying dynamics within an interpretable model structure. An additive model structure is adopted for this purpose, which offers enhanced parsimony and is shown to be particularly suitable for mechanical systems. The proposed approach integrates the recursive simplified refined instrumental variable method with block-coordinate descent to minimize an exponentially-weighted output error cost function. This novel recursive identification method delivers parametric continuous-time additive models and is applicable in both open-loop and closed-loop controlled systems. Its efficacy is shown using numerical simulations and is further validated using experimental data to detect the time-varying resonance dynamics of a flexible beam system. These results demonstrate the effectiveness of the proposed approach for online and interpretable estimation for advanced monitoring and control applications.

Mechanical ventilators are complex mechatronic devices that are essential for patients who are unable to breathe independently. The aim of this paper is to develop a systematic control method that achieves accurate tracking of both the pressure and flow to ensure comfortable breathing for the patient. This is achieved by using a feedback design procedure technique based on static decoupling and the factorized Nyquist criterion. Furthermore, switching controllers are introduced that allow for improved baseflow tracking performance. The presented control method is implemented in a real ventilator and it is demonstrated that the tracking performance is improved by conducting an experimental case-study.

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.