5 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…
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…
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…
On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
Braghadeesh Lakshminarayanan, Cristian R. Rojas
In this paper, we analyze the asymptotic properties of the Two-Stage (TS) estimator -- a simulation-based parameter estimation method that constructs estimators offline from synthe…