2008
DOI: 10.1002/oca.843
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Optimal control problems with delays in state and control variables subject to mixed control–state constraints

Abstract: Optimal control problems with delays in state and control variables are studied. Constraints are imposed as mixed control-state inequality constraints. Necessary optimality conditions in the form of Pontryagin's minimum principle are established. The proof proceeds by augmenting the delayed control problem to a nondelayed problem with mixed terminal boundary conditions to which Pontryagin's minimum principle is applicable. Discretization methods are discussed by which the delayed optimal control problem is tra… Show more

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Cited by 228 publications
(196 citation statements)
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“…To minimize the Hamiltonian functional, the Pontryagian's minimum principle [12] is used. Thus, we arrive at the following theorem.…”
Section: Necessary Conditions For Optimalitymentioning
confidence: 99%
“…To minimize the Hamiltonian functional, the Pontryagian's minimum principle [12] is used. Thus, we arrive at the following theorem.…”
Section: Necessary Conditions For Optimalitymentioning
confidence: 99%
“…Pontryagin's minimum Principle with delay given in [16] provides necessary conditions for an optimal control problem. This principle converts (2), (4), and (5) into a problem of maximizing an Hamiltonian, H, with…”
Section: Optimalitymentioning
confidence: 99%
“…By applying Pontryagin's minimum principle with delay in state [16], we obtain the following theorem. …”
Section: Optimalitymentioning
confidence: 99%
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“…The delay(s) may appear in the system state, control input and/or output. Delays occur frequently in incubation periods, mechanics, viscoelasticity, physics, physiology, population dynamics, communication, information technologies, stability of networked control systems, maturation times, age structure, blood transfusions, biological, chemical, electronic and transportation systems [1][2][3]. Therefore the control of time-delay systems has been interested by many engineers and scientists, due to its variety presence in realistic models of phenomena.…”
Section: Introductionmentioning
confidence: 99%