Double-layered model predictive control of state-space model
XIE Ya-jun
DING Bao-cang
CHEN Qiao
Abstract：The so-called double-layered model predictive control (MPC) performs firstly the setpoint optimization, then the setpoint tracking. Based-on the existing double-layered dynamic matrix control, this paper gives an algorithm for double-layered MPC based on the state-space model. Based on the disturbance model and the newly defined open-loop pre-dictions, this algorithm proposes a new open-loop prediction module. This open-loop prediction module adopts the Kalman filter to obtain the open-loop dynamic/steady-state predictions of manipulated/controlled variables (MVs/CVs). Based on these open-loop predictions, the steady-state target calculation (SSTC) module is the same as in double-layered dynam-ic matrix control, but its details obey the state-space method. Based on the steady-state targets (setpoints) of MVs/CVs provided by SSTC, the dynamic control module computes the control moves by solving the quadratic programming. The numerical example verifies the effectiveness of the proposed algorithm.
Keywords：model predictive control (MPC)state-spaceKalman filtersetpoint optimizationdouble-layered structure
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 69-76 )