The performance of model-predictive control (MPC) is affected by many factors. Considering that current model quality index does not take the influence of disturbance into consideration

two indices are applied to realize real-time monitoring of system performance: historical performance index based on MPC objective function and covariance index based on model predictive residuals. The former monitors the whole performance and the latter reflects the influence of the model mismatch and the disturbance. They respond differently to different factors. Combining the re-identified result of the disturbance innovations

we can get preliminary diagnosis why the system performance decreases and narrow the scope of the source of performance degradation. Finally

the experimental validation on Wood-berry column demonstrates the effectiveness of this method.