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  • 标题:Disturbance modeling and state estimation for offset-free predictive control with state-space process models
  • 本地全文:下载
  • 作者:Piotr Tatjewski
  • 期刊名称:International Journal of Applied Mathematics and Computer Science
  • 电子版ISSN:2083-8492
  • 出版年度:2014
  • 卷号:24
  • 期号:2
  • DOI:10.2478/amcs-2014-0023
  • 出版社:De Gruyter Open
  • 摘要:Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2 x 2 example process problem
  • 关键词:model predictive control; state-space models; disturbance rejection; state observer; Kalman filter
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