Setpoint control of bilinear systems from noisy data

Abstract

We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it. The controller should achieve regulation to a given state setpoint and provide a guaranteed basin of attraction. Determining the equilibrium input to achieve that setpoint is not trivial in a data-based setting and we propose the design of a controller in two scenarios. The design takes the form of linear matrix inequalities and is validated numerically for a Cuk converter.

References in corpus (5)

• Data-driven control via Petersen's lemma
• Combining Prior Knowledge and Data for Robust Controller Design
• Trade-offs in learning controllers from noisy data
• Data-Driven Optimal Control of Bilinear Systems
• Data-driven design of safe control for polynomial systems