A method of MPC model error detection

This work introduces a method of multivariable model error detection in model prediction control (MPC). The idea is to use non-disturbing small sinusoidal test signals to obtain accurate estimates of process frequency responses at several frequency points. Then, the differences between estimated frequency responses and the frequency responses of current MPC model are used to form the model error index matrix which is used to access the model error of the MPC controller. An upper error bound is developed for quantifying the error of frequency response estimation. The method works in closed-loop operation with the MPC controller online. Simulation studies are used to demonstrate the use of the method.

► A method of multivariable model error detection in model prediction control (MPC) is introduced.
► Non-disturbing small sinusoidal test signals are used to obtain accurate estimates of process frequency responses at 3 frequency points.
► Model error index matrix is used to access the model error of the MPC controller. An upper error bound is developed for quantifying the error of frequency response estimation.
► The method works in closed-loop operation with the MPC controller online. Simulation studies are used to demonstrate the use of the method.