Simulation-driven fixed-order controller tuning via moment matching

We propose a controller tuning method based on the data-driven model reduction by moment matching theory. By selecting a reference closed-loop transfer function, a moment matching data-driven model reduction algorithm is used to synthesize a fixed-order controller, via the identification of a model of the inverse transfer function of the controller, i.e. the transfer function from the controlled input of the system to the mismatch error signal. The controller is finally obtained by inverting this transfer function. The fixed-order controller is guaranteed to match the steady-state behavior of the ideal controller at certain pre-selected frequencies. The effectiveness of the resulting design method is assessed on a control problem for the 48-th order model of the Los Angeles University Hospital building.