8 papers
Fine Tuning a Simulation-Driven Estimator
Braghadeesh Lakshminarayanan, Margarita A. Guerrero, Cristian R. Rojas
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…
Sample-Efficient Counterfactual Tuning for Compressor Pressure Control
Margarita A. Guerrero, Rodrigo A. González, Cristian R. Rojas
In controlled industrial environments, ensuring safety and performance during controller tuning is a challenging and critical task. In particular, control loops in compressor-plenu…
Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems
Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas
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…
ACE: Adapting sampling for Counterfactual Explanations
Margarita A. Guerrero, Cristian R. Rojas
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 outpu…
Data-Driven Estimation of Structured Singular Values
Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas
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 mann…
A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior
Yibo Shi, Braghadeesh Lakshminarayanan, Cristian R. Rojas
Inference and estimation are fundamental in statistics, system identification, and machine learning. When prior knowledge about the system is available, Bayesian analysis provides…