MPC relevant identification––tuning the noise model

The philosophy of identification by minimizing an objective function that is commensurate with the control objective function is called control relevant identification. The control relevant method studied in this paper minimizes a multistep ahead prediction error objective function, suitable for model predictive controllers, to obtain an optimal multistep ahead predictor. It is shown that the method described in this paper provides better designed performance of the controller. A number of properties of this method in the context of FIR models are presented in this paper. The noise model plays a pivotal role in determining the performance of multistep ahead prediction errors. A method for tuning the noise model using the proposed control relevant method is presented in this paper.