A Scheme of Model Invalidation Assessment for Multivariable Dynamic Matrix Controllers

Model mismatch is an inherent problem in model predictive control (MPC), but the performance of MPC will descend and even be invalid when the model mismatch extent exceeds a certain level. This paper introduces a mechanism for assessing model invalidation for dynamic matrix control (DMC) in a probabilistic framework. The relationship between model predictive error and disturbance series, which can determine model-plant mismatch (MPM), is presented using internal model control (IMC) structure of DMC. Then the difference between these two time series is used to assess model invalidation through statistical inference method. The model is invalid if the calculated statistics is larger than a threshold with a given significant level. Numerical examples demonstrate the effectiveness of proposed method.