Speaker
Description
Over the past years, there has been extensive research in the control systems community on data-based approaches for the analysis and synthesis of dynamical systems. A distinguishing feature of this “new wave” of data-driven methods is the emphasis on solutions and techniques with provable guarantees regarding the (robust) stability and performance of the considered (closed-loop) systems. In this talk, we present results that contribute to this area of research.
We present a non-conservative controller synthesis method for discrete-time linear time-invariant systems. The goal of the synthesis is to render the closed-loop system dissipative with respect to a given, generic, unstructured quadratic supply function. Both static state-feedback control and dynamic output-feedback control are considered, with the latter restricted to systems of a specific autoregressive form. The plant model is assumed to be (partially) unknown, but instead we require knowledge of trajectories in the control channel, i.e., the controlled input and measured state/output available for control. It is assumed that these trajectories are corrupted by bounded noise. Replacing a model by the recorded finite length trajectories is what makes the presented approach a data-based approach. The resulting controllers are guaranteed to achieve, in a non-conservative way, the closed-loop stability and the desired dissipativity properties for the entire set of systems consistent with the (noisy) data. The controller synthesis method is based on a convexification procedure that leverages the dualization lemma.
