Complex engineering applications may require dealing with dynamics that cannot be easily modeled and included in a control framework. In addition, an uncertain environment might expose the system to disturbances that are unforeseeable during the control design phase. In this presentation, a solution to control uncertain constrained systems is proposed. The objective is twofold: track a reference signal in presence of unmodelled dynamics, and enforce the system state and input constraints. Under the assumption that the linear dominant plant dynamics are available, a Model Reference Adaptive Control strategy is employed to handle the unknown system dynamics. The proposed controller is then enhanced with a Robust Command Governor scheme to enforce the system constraints. Unlike other optimization-based methods, such as Model Predictive Control, Reference Governors (RG) and Command Governors (CG) do not act directly on the closed-loop dynamics; instead, they evaluate the desired reference signal and predict the closed-loop states on a predefined horizon. If constraints are not satisfied, then RG and CG compute the closest signal to the desired reference and use it as a virtual reference in the closed-loop system. The optimization-based process is then repeated at each time step. Numerical simulations illustrate the methodology applied to a geostationary satellite subject to unmodelled dynamics.
Lieu : grande salle de réunions dans la coursive du bâtiment R de l'ONERA à Toulouse.
Lien visio : https://rdv.onera.fr/seminaireDTIS