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arXiv · 2410.24167

Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems

Alessandro Bosso·Marco Borghesi·Andrea Iannelli·Giuseppe Notarstefano·Andrew R. Teel
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Abstract

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples.

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Alessandro Bosso, Marco Borghesi, Andrea Iannelli, Giuseppe Notarstefano, Andrew R. Teel. 2024-10-31. Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems. https://arxiv.org/abs/2410.24167

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