Data-Driven Command Governors for Discrete-Time LTI Systems with Linear Constraints

The Command Governor (CG) approach effectively addresses the problem of enforcing constraints on precompensated systems without modifying existing controllers. However, the prediction model dependence limits its use in cost-sensitive parameter identification applications. Inspired by the recent development of several Data-driven Predictive Control (DPC) algorithms and leveraging behavioral systems theory, this paper proposes a novel data-driven Command Governor scheme that bypasses explicit modeling and does not rely on a parametric system representation. By means of using an input/output trajectory of the plant and a representation of the controller, the proposed data-driven CG handles explicitly both input and output constraints. The effectiveness of the proposed approach is validated through an illustrative example.