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Reduction of the Number of Variables in Parametric Constrained Least-Squares Problems
Alberto Bemporad, Gionata Cimini
For linearly constrained least-squares problems that depend on a vector of parameters, this paper proposes techniques for reducing the number of involved optimization variables. Af…
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?
Sebastien Gros, Mario Zanon, Alberto Bemporad
For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy. Among other things, guaranteeing…
Preference-based MPC calibration
Mengjia Zhu, Alberto Bemporad, Dario Piga
Automating the calibration of the parameters of a control policy by means of global optimization requires quantifying a closed-loop performance function. As this can be impractical…
Constrained Controller and Observer Design by Inverse Optimality
Mario Zanon, Alberto Bemporad
Model Predictive Control (MPC) is often tuned by trial and error. When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is intro…