Robust Data-Driven Predictive Control for Linear Time-Varying Systems

Abstract

This letter presents a new robust data-driven predictive control scheme for linear time-varying (LTV) systems with unknown nominal system models. To tackle the challenges arising from the unknown nominal model and the time-varying nature of the system, a data-dependent optimization problem is formulated using input-state-output data. It calculates an upper bound on the objective function and, at the same time, designs a state feedback controller to minimize the bound. Moreover, two significant concerns, namely the feasibility of the optimization problem and the stability of the closed-loop system under the designed controller, are thoroughly investigated. Compared with the existing data-enabled predictive control method for LTV systems, the proposed control scheme does not require the collected data to satisfy the persistently exciting (PE) condition and uniformly exponentially stabilizes the system. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.