2016
DOI: 10.1007/s10957-016-0918-x
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Galerkin Optimal Control

Abstract: Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington ABSTRACT (maximum 200 words)A Galerkin-based family of numerical… Show more

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Cited by 6 publications
(7 citation statements)
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“…The Galerkin formulation with weak imposition of endpoint conditions may also allow for problem discretizations with other than Legendre-Gauss-Lobatto (LGL) nodes, such as Legendre-Gauss (LG) and LegendreGauss-Radau (LGR) points. This is discussed in some detail in Boucher (2015).…”
Section: Galerkin Optimal Controlmentioning
confidence: 99%
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“…The Galerkin formulation with weak imposition of endpoint conditions may also allow for problem discretizations with other than Legendre-Gauss-Lobatto (LGL) nodes, such as Legendre-Gauss (LG) and LegendreGauss-Radau (LGR) points. This is discussed in some detail in Boucher (2015).…”
Section: Galerkin Optimal Controlmentioning
confidence: 99%
“…For the reason of space, the feasibility of the computational strategy is not proved in this paper. However, some similar proofs are provided in Boucher (2015) as reference.…”
Section: Computational Strategymentioning
confidence: 99%
“…where y p = y + d is the practical output and y m is the output of an independent model. Here, the independent model is simply defined as the same as models (18), (19), (20), (21), (22), and (23). To this end, the estimated disturbancesd are fed back instead of d in (33) to implement the receding Galerkin method.…”
Section: Receding Galerkin Optimal Control With An Independentmentioning
confidence: 99%
“…In this way, the estimate error e will just approach zero. (27) do in fact exist by selecting appropriate feasibility tolerance δ N and the order of approximation N, as remarked in [21,23]. Precise bounds for δ N can be found experimentally using a recursive refinement process through increasing the order of approximation N until all the constraints in a nonlinear programming problem like (27) are satisfied.…”
Section: Receding Galerkin Optimal Control With An Independentmentioning
confidence: 99%
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