@ARTICLE{Tuan_Truong_Thanh_Disturbance-Kalman_2022,
 author={Tuan, Truong Thanh and Zabiri, Haslinda and Mutalib, Mohammad Ibrahim Abdul and No, Dai-Viet N.},
 volume={vol. 32},
 number={No 1},
 journal={Archives of Control Sciences},
 pages={153-173},
 howpublished={online},
 year={2022},
 publisher={Committee of Automatic Control and Robotics PAS},
 abstract={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.},
 type={Article},
 title={Disturbance-Kalman state for linear offset free MPC},
 URL={http://ochroma.man.poznan.pl/Content/122927/PDF/art08_internet.pdf},
 doi={10.24425/acs.2022.140869},
 keywords={Kalman filter, plant-model mismatch, offset free MPC, disturbance model},
}