2019
DOI: 10.1109/lcsys.2018.2851539
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Constrained Order Observer Design for Disturbance Decoupled Output Estimation

Abstract: DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal… Show more

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Cited by 1 publication
(3 citation statements)
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“…in which 1 = 1 1 1 , 2 = 2 2 2 , and the constant matrices ℎ and denote, respectively, the productintegration-matrix of two OF basis vectors having different time intervals [22]. From (19), (21), and (22), it can be seen that the OFA-based computational approach does not limit the sizes of both ℎ and , where ℎ is the given known constant time delay and is the final time which is given by the control engineer for desiring state estimation error decreased to almost zero.…”
Section: Optimal Design Of the Observer Gain Matrixmentioning
confidence: 99%
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“…in which 1 = 1 1 1 , 2 = 2 2 2 , and the constant matrices ℎ and denote, respectively, the productintegration-matrix of two OF basis vectors having different time intervals [22]. From (19), (21), and (22), it can be seen that the OFA-based computational approach does not limit the sizes of both ℎ and , where ℎ is the given known constant time delay and is the final time which is given by the control engineer for desiring state estimation error decreased to almost zero.…”
Section: Optimal Design Of the Observer Gain Matrixmentioning
confidence: 99%
“…First, randomly generate the initial population with the chromosomes of form̃= [ 11 , 12 , ⋅ ⋅ ⋅ , ] for executing the HTGA. Next, compute the solutions of̃1 and̃2 by applying (19) and (21), and compute by using (22) which is defined as the fitness function of the HTGA. Then, the fitness values of the initial population feasible for the LMI-based constraint in (13) are calculated.…”
Section: Optimal Observer Design Proceduresmentioning
confidence: 99%
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