Data-driven feedback passivation of switched linear systems
Meichen Guo (TU Delft - Mechanical Engineering)
Sergio Grammatico (TU Delft - Mechanical Engineering)
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Abstract
We propose data-driven passivation strategies for switched linear systems with unknown system matrices. In particular, we consider two types of switching laws: controlled state-dependent switching and exogenous switching. Using offline open-loop input-state-output data, we derive linear state feedback controllers that render the switched system strictly passive via bilinear matrix inequalities (BMIs). Furthermore, passivation with noisy data, relaxation of the BMIs, and data-driven stabilization via passivity are discussed. Finally, numerical simulation results validate the performance of the proposed data-driven controllers.
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File under embargo until 07-02-2027