Issues in performance diagnostics of model-based controllers

The task of diagnosing poorly performing controllers is a challenging one. Process design, controller design, changing process conditions are amongst the common causes of poor performance. In this work, we quantify the effect of factors such as constraints, modelling uncertainty, disturbance uncertainty, process nonlinearity on the achieved closed loop performance. It is important to know the contribution of these factors on the achieved performance in order to attempt diagnosis. The results derived are applicable to the case of deterministic as well as stochastic inputs. Illustrative examples, both SISO and MIMO, are used to demonstrate the key results.