Low order sampled-data control

A method for obtaining low order sampled-data H_∞ controllers is presented. The method is mainly based on a parametric static feedback controller for a plant that is augmented with the controller dynamics. The design of a full-order controller is a convex problem, while the optimisation problem for lower order controllers is non convex. The proposed method starts with design of a full-order sampled-data controller using Riccati equations. Then this controller is reduced by an ordinary model reduction technique, and the reduced controller is used as an initial value for an iterative procedure using linear matrix inequalities (LMIs) in the search for an optimal controller. The matrix inequalities are in fact linear in either the Lyapunov matrix or the static controller matrix, why the solution to the non convex problem fundamentally is given by a bilinear matrix inequality (BMI). The order of the controller is reduced until the closed loop performance degrades too much. Simulations are shown for the control of a time delayed SISO-plant where the controller order can be reduced from 8th to 3rd order. Results are also shown from control of a MIMO-model of a jet engine where the reduction is successful from 15th to 4th order. It is argued that the non convexity is handled efficiently since the procedure uses a model reduction of the full-order controller as initial value.