Disturbance Modeling and State Estimation for Predictive Control with Different State-Space Process Models
AbstractThe paper is conce rned with d isturbance modeling a nd observer design for ModelPredictive Control (MPC) with d ifferent formulations of state-space proce ss models. Systematicdiscuss ion is given, presenting the ways the deterministic disturbance s most important in proce sscontrol app lications s hould be treated in the MPC algorithm, to o btain disturbance att enu ationand offset-free c ontrol. The c losely related p roblem of observer design and understand ing isexplained. It is shown that a simple app roa ch with a proce ss s tate observer only, in the presenceof deterministic disturbance s, can work bett er than the c onventional app roa ch of extend edproce ss -disturbance state e stimation. The observer design is also considered for the c ase ofextend ed velocity form state-space modeling including integrators. A simplified app roa ch withredu ce d order observer is given. The results are illustrated on a 2×2 example proce ss problem.
|Publication size in sheets||0.5|
|Book||Proceedings of the 18th IFAC World Congress, IFAC-PapersOnLine, 2011, Elsevier Science Direct, ISBN 978-390266193-7|
|Keywords in English||Model predictive c ontrol, state-space models, disturbance rejec tion, state observers,extend ed state-space modeli|
|Publication indicators||= 4; = 3.0; : 2011 = 0.345|
|Citation count*||3 (2016-07-04)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.