arXiv subjects

Margarita A. Guerrero

Publications and source records attributed to Margarita A. Guerrero.

7 recordsLinked to original sources

arXiv2026

UQ-ACE: Stability-Aware Counterfactual Controller Tuning via Bayesian SINDy

Retuning feedback controllers in critical processes is challenging when experiments are costly, open-loop excitation is infeasible, and candidate controllers must be screened before deployment. The existing Adaptive Sampling for Counterfactual Explanations (ACE) method provides a sample-efficient approach for finding minimal controller-parameter changes that recover prescribed closed-loop performance, but it does not explicitly account for stability of candidate controllers proposed during the search, which may be destabilizing under plant uncertainty. This paper introduces UQ-ACE, a stability-aware retuning method based on Bayesian SINDy. In UQ-ACE, closed-loop trajectories are used to infer a posterior distribution over local dynamical models, from which candidate controllers are screened for stability before implementation. Extensive simulations show that UQ-ACE achieves valid retunings with few closed-loop experiments while preventing deployment of unstable candidates.

math.OC↗

arXiv2026

Sample-Efficient Counterfactual Tuning for Compressor Pressure Control

In controlled industrial environments, ensuring safety and performance during controller tuning is a challenging and critical task. In particular, control loops in compressor-plenum-throttle systems cannot tolerate costly interruptions, and aggressive excitation may lead to unsafe operating regimes. Given the wide availability of historical data, this paper introduces a counterfactual explainability approach for sample-efficient retuning of compressor control loops. The proposed data-driven algorithm determines, without an explicit plant model or previous control law, the smallest controller adjustment required to achieve predefined performance specifications while guaranteeing stability. The effectiveness of the method is demonstrated through an extensive Monte Carlo simulation study.

math.OC↗

arXiv2026

Data-Driven Estimation of Vinnicombe metric

Quantifying model mismatch in a control-relevant manner is fundamental in robust control. A well-known metric for this purpose is the $ν$-gap, or Vinnicombe metric, which measures the discrepancy between a nominal model and the real system from a closed-loop viewpoint. However, its computation typically requires explicit knowledge of the true system. In this letter, we propose an identification-free, data-driven method to estimate the $ν$-gap between discrete-time SISO systems directly from input-output experiments. The method is complemented by a data-driven winding-number test, based on Welch-type averaging, to verify a required topological condition for the computation of the metric. Numerical simulations on heavy-duty gas-turbine models and a textbook example show that the proposed estimate closely matches MATLAB$^©$ \texttt{gapmetric}, while correctly detecting cases in which the admissibility conditions fail.

math.OC↗

arXiv2026

Fine Tuning a Simulation-Driven Estimator

Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations.

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arXiv2025

ACE: Adapting sampling for Counterfactual Explanations

Counterfactual Explanations (CFEs) interpret machine learning models by identifying the smallest change to input features needed to change the model's prediction to a desired output. For classification tasks, CFEs determine how close a given sample is to the decision boundary of a trained classifier. Existing methods are often sample-inefficient, requiring numerous evaluations of a black-box model -- an approach that is both costly and impractical when access to the model is limited. We propose Adaptive sampling for Counterfactual Explanations (ACE), a sample-efficient algorithm combining Bayesian estimation and stochastic optimization to approximate the decision boundary with fewer queries. By prioritizing informative points, ACE minimizes evaluations while generating accurate and feasible CFEs. Extensive empirical results show that ACE achieves superior evaluation efficiency compared to state-of-the-art methods, while maintaining effectiveness in identifying minimal and actionable changes.

cs.LG↗

arXiv2025

Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems

Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices -- selected online via a measurable scheduling variable -- thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.

math.OC↗

arXiv2025

Data-Driven Estimation of Structured Singular Values

Estimating the size of the modeling error is crucial for robust control. Over the years, numerous metrics have been developed to quantify the model error in a control relevant manner. One of the most important such metrics is the structured singular value, as it leads to necessary and sufficient conditions for ensuring stability and robustness in feedback control under structured model uncertainty. Although the computation of the structured singular value is often intractable, lower and upper bounds for it can often be obtained if a model of the system is known. In this paper, we introduce a fully data-driven method to estimate a lower bound for the structured singular value, by conducting experiments on the system and applying power iterations to the collected data. Our numerical simulations demonstrate that this method effectively lower bounds the structured singular value, yielding results comparable to the MATLAB$^©$ Robust Control Toolbox.

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