TY  - JOUR
AV  - none
N1  - cited By 1
TI  - Disturbance-Kalman state for linear offset free MPC
SP  - 153
SN  - 12302384
PB  - Polska Akademia Nauk
EP  - 173
IS  - 1
N2  - In model predictive control (MPC), methods of linear offset free MPC are well established such as the disturbance model, the observer method and the state disturbance observer method. However, the observer gain in those methods is difficult to define. Based on the drawbacks observed in those methods, a novel algorithm is proposed to guarantee offset-free MPC under model-plant mismatches and disturbances by combining the two proposed methods which are the proposed Recursive Kalman estimated state method and the proposed Disturbance-Kalman state method. A comparison is made from existing methods to assess the ability of providing offset-free MPC onWood-Berry distillation column. Results shows that the proposed offset free MPC algorithm has better disturbance rejection performance than the existing algorithms. © 2022. The Author(s).
KW  - Distillation columns; Disturbance rejection; Predictive control systems
KW  -  Disturbance models; Disturbance observer; Free model; Model-predictive control; Novel algorithm; Observer gain; Offset free model predictive control; Plant model mismatches; Predictive control methods; State disturbance
KW  -  Model predictive control
ID  - scholars17656
Y1  - 2022///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130290108&doi=10.24425%2facs.2022.140869&partnerID=40&md5=d55940b2ab86eccbcfd2d41d9822d878
A1  - Tuan, T.T.
A1  - Zabiri, H.
A1  - Mutalib, M.I.A.
A1  - VO, D.-V.N.
JF  - Archives of Control Sciences
VL  - 32
ER  -