Abstract:Abstract. The method presented here is an extension of the multiple shooting algorithm in order to handle multipoint boundary-value problems and problems of optimal control in the special situation of singular controls or constraints on the state variables. This generalization allows a direct treatment of (nonlinear) conditions at switching points. As an example a model of optimal heating and cooling by solar energy is considered. The model is given in the form of an optimal control problem with three control … Show more
“…On a boundary arc with x(t) = 2 0.05 it follows from u = -As, (12) and (15) that Xe = 0 and X, = 0. Then (14) and the sign condition (47) imply the desired…”
Section: Necessary Conditions Of Optimalitymentioning
SUMMARYThe non-linear beam with bounded deflection is considered as an optimal control problem with bounded state variables. The theory of necessary optimality conditions leads to boundary value problems with jump conditions which are solved by multiple-shooting techniques. A branching analysis is performed which exhibits the different solution structures. In particular, the second bifurcation point is determined numerically. The bifurcation diagram reveals a hysteresis-like behaviour and explains the jumping to a different state at this bifurcation point.
“…On a boundary arc with x(t) = 2 0.05 it follows from u = -As, (12) and (15) that Xe = 0 and X, = 0. Then (14) and the sign condition (47) imply the desired…”
Section: Necessary Conditions Of Optimalitymentioning
SUMMARYThe non-linear beam with bounded deflection is considered as an optimal control problem with bounded state variables. The theory of necessary optimality conditions leads to boundary value problems with jump conditions which are solved by multiple-shooting techniques. A branching analysis is performed which exhibits the different solution structures. In particular, the second bifurcation point is determined numerically. The bifurcation diagram reveals a hysteresis-like behaviour and explains the jumping to a different state at this bifurcation point.
“…Let (x, ū, v) ∈ C be a critical direction. Define ( ξ, ȳ) by the transformation (47) and set h := ȳT . Note that ( 14)-( 15) yield Dη j (x 0 , xT )( ξ0 , ξT + F v,T h) = 0, for j = 1, .…”
Section: Tranformed Critical Conesmentioning
confidence: 99%
“…Remark 4.2. Note that P consists of the directions obtained by transformating the elements of C via (47).…”
Section: Tranformed Critical Conesmentioning
confidence: 99%
“…Throughout this proof, whenever we put ρ i we refer to a positive constant depending on A ∞ , F v ∞ , E ∞ , and/or B ∞ . Let (x, ū, v) ∈ H 2 and ( ξ, ȳ) be defined by Goh's Transformation (47). Thus ( ξ, ū, ȳ) is solution of (48).…”
In this article we study optimal control problems for systems that are affine in one part of the control variable. Finitely many equality and inequality constraints on the initial and final values of the state are considered. We investigate singular optimal solutions for this class of problems. First, we obtain second order necessary and sufficient conditions for weak optimality. Afterwards, we propose a shooting algorithm and show that the sufficient condition above-mentioned is also sufficient for the local quadratic convergence of the algorithm.
“…These conditions can be utilized to obtain an analytic solution for simple problems described by a few dynamic equations (1-3). However, for problems described by a large number of dynamic equations [3,4] Stutts (23) Thomas (18] Jacobson [24] Edgar and Lapidus [25,26] Maurer [27] Oberele (28) Aly (29) Aly and Chan [30] Aly and Megeed (31) Soliman and Ray (32) Kumar [33) Jacobson [34] Cuthrell and Biegler [29] Downloaded by [University of California Santa Barbara] at 04: 20 17 June 2016 SINGULAR CONTROL PROBLEMS 167 (say 4 or more) numerical solutions are inevitable. There have been a number of numerical techniques proposed for solving singular control problems, which are summarized in Table II.…”
A unidirectional method is developed to solve optimal singular control problems with bounded state variables. The computational algorithm utilizes the necessary conditions of optimality and an unidirectional scheme to reduce the iterations required in two-point boundary value problems. The advantage of the method is that it satisfies all the necessary conditions of optimality and results in a considerable reduction in computational effort. Three numerical examples illustrate the use of these algorithms for solving several chemical reaction engineering problems.
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