C
Cristian R. Rojas
Researcher at Royal Institute of Technology
Publications - 241
Citations - 2313
Cristian R. Rojas is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: System identification & Estimator. The author has an hindex of 23, co-authored 223 publications receiving 2030 citations. Previous affiliations of Cristian R. Rojas include Federico Santa María Technical University & University of British Columbia.
Papers
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Journal ArticleDOI
Robust optimal experiment design for system identification
TL;DR: This paper develops the idea of min-max robust experiment design for dynamic system identification and proposes a convex optimisation algorithm that can be applied more generally to a discretised approximation to the design problem.
Journal ArticleDOI
A Note on the SPICE Method
TL;DR: This result positions SPICE as a computationally efficient technique for the calculation of Lasso-type estimators and establishes its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso.
Journal ArticleDOI
Iterative Data-Driven H-infinity Norm Estimation of Multivariable Systems With Application to Robust Active Vibration Isolation
TL;DR: A new data-driven H∞ norm estimation algorithm for model-error modeling of multivariable systems that requires significantly a fewer prior assumptions on the true system, hence it provides stronger guarantees in a robust control design.
Proceedings ArticleDOI
Model predictive control with integrated experiment design for output error systems
Christian A. Larsson,Mariette Annergren,Håkan Hjalmarsson,Cristian R. Rojas,Xavier Bombois,Ali Mesbah,Per Erik Moden +6 more
TL;DR: This contribution combines MPC with experiment design to formulate a control problem where excitation constraints are included and the benefits are that time domain constraints are respected while the experiment design criteria are fulfilled.
Posted Content
On change point detection using the fused lasso method
Cristian R. Rojas,Bo Wahlberg +1 more
TL;DR: This paper establishes the (approximate) sparse consistency properties, including rate of convergence, of the so-called fused lasso signal approximator (FLSA), and shows that this estimator is otherwise incapable of correctly detecting the underlying sparsity pattern.