Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
arXiv:2004.11630 · doi:10.1016/j.sysconle.2020.104788
Abstract
Motivated by the goal of having a building block in the direct design of data-driven controllers for nonlinear systems, we show how, for an unknown discrete-time bilinear system, the data collected in an offline open-loop experiment enable us to design a feedback controller and provide a guaranteed under-approximation of its basin of attraction. Both can be obtained by solving a linear matrix inequality for a fixed scalar parameter, and possibly iterating on different values of that parameter. The results of this data-based approach are compared with the ideal case when the model is known perfectly.
References in corpus (6)
- End to End Learning for Self-Driving Cars
- Safe Model-based Reinforcement Learning with Stability Guarantees
- Robust data-driven state-feedback design
- Data-Driven LQR Control Design
- Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control?
- Learning control for polynomial systems using sum of squares relaxations
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