Adaptive Data-Driven Control for Linear Time Varying Systems

In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed unknown, and no information in regard its behaviour exploited. First, using set-membership identification techniques, formulate the controller design problem through a model-matching scheme, i.e., designing such that closed-loop matches of given reference model. then reformulated as derive corresponds minimum variation bounding parameters. Finally, convex relaxation proposed solve formulated means programming. effectiveness scheme demonstrated two simulation examples.