Abstract
This paper presents a methodology for enhancing the robustness of a generalized predictive control (GPC) controlled system by convex optimization of the Youla parameter. This methodology requires, as a first step, the design of an initial GPC controller; this controller is then robustified considering frequency and time-domain constraints. By means of the Youla parametrization, frequency and time-domain constraints are formulated within a convex optimization framework, then the optimal parameter is deduced solving this optimization problem. The developed robustified GPC controller is finally applied on a benchmark including an induction motor, aiming at reducing the impact of measurement noise and inertia variation of the system, while respecting a time-domain template for the disturbance rejection. Comparison with results obtained with a more classical proportional-integral-derivative (PID) controller is finally given.
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