Performance monitoring of model-predictive controllers via model residual assessment

Model quality is a main factor that affects the control performance of model-based controllers. In this paper, a new closed-loop model assessment approach is proposed to assess model deficiency from routine closed-loop data. The proposed model quality index is a minimum variance benchmark for the model residuals obtainable from closed-loop data. From the feedback invariant principle the disturbance innovations are shown to be unaffected by the feedback controller. Then it is shown that the disturbance innovations can be estimated from closed loop data by an orthogonal projection of the current output onto the space spanned by past outputs, inputs or setpoints. With the estimated disturbance innovations as the benchmark, a model quality index is developed by using the ratio of a quadratic form of model residuals and that of the estimated disturbance innovations. The effectiveness of the proposed methods is demonstrated by simulations.

► A new closed-loop model assessment approach is proposed to assess model quality from routine closed-loop data.
► The benchmark has a lower bound obtainable from closed-loop data.
► The disturbance innovations are shown to be unaffected by the feedback controller.
► A model quality index is developed by using the ratio of the model residuals to that of the estimated innovations benchmark.