Control-relevant prefiltering: a systematic design approach and case study

The authors examine the use of control-relevant prefiltering applied to parameter estimation using prediction-error methods. The prefiltering step ensures that the estimated model retains those plant characteristics that are most significant with regards to the user´s control requirements. They describe how to systematically build the prefilter in terms of the estimated model structure, the desired closed-loop speed-of-response, and the setpoint/disturbance characteristics of the control problem. Two implementation algorithms are presented which are applied to the plant data obtained from a distillation column. The results show that substantial improvements are obtained from control-relevant prefiltering in output error and partial least-squares estimation, while some caution must be exercised when applied to FIR and low-order ARX estimation