On data-driven stabilization of systems with quadratic nonlinearities

Abstract

In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length experiment. Necessary and sufficient conditions are derived to guarantee that the system is absolutely stabilizable and a controller is designed. Results derived under some relaxed prior information about the system and strengthened data assumptions are also discussed. Numerical examples illustrate the method with different levels of prior information.

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• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Deep learning for universal linear embeddings of nonlinear dynamics
• Robust data-driven state-feedback design
• Combining Prior Knowledge and Data for Robust Controller Design
• Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
• On the Sample Complexity of Data-Driven Inference of the ${\mathcal{L}}_2$-gain

Cited by in corpus (1)

• Combining Prior Knowledge and Data for Robust Controller Design