Relevant bibliographies by topics / Optimal control

Academic literature on the topic 'Optimal control'

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Published: 4 June 2021
Last updated: 13 July 2024

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Journal articles on the topic "Optimal control"

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James, M. R. "Optimal Quantum Control Theory." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 343–67. http://dx.doi.org/10.1146/annurev-control-061520-010444.

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This article explains some fundamental ideas concerning the optimal control of quantum systems through the study of a relatively simple two-level system coupled to optical fields. The model for this system includes both continuous and impulsive dynamics. Topics covered include open- and closed-loop control, impulsive control, open-loop optimal control, quantum filtering, and measurement feedback optimal control.
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Iskenderov, A. D., and R. K. Tagiyev. "OPTIMAL CONTROL PROBLEM WITH CONTROLS IN COEFFICIENTS OF QUASILINEAR ELLIPTIC EQUATION." Eurasian Journal of Mathematical and Computer Applications 1, no. 1 (2013): 21–38. http://dx.doi.org/10.32523/2306-3172-2013-1-2-21-38.

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Trofimov, A. M., and V. M. Moskovkin. "Optimal control over geomorphological systems." Zeitschrift für Geomorphologie 29, no. 3 (October 31, 1985): 257–63. http://dx.doi.org/10.1127/zfg/29/1985/257.

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Goncharenko, Borys, Larysa Vikhrova, and Mariia Miroshnichenko. "Optimal control of nonlinear stationary systems at infinite control time." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 4(35) (2021): 88–93. http://dx.doi.org/10.32515/2664-262x.2021.4(35).88-93.

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The article presents a solution to the problem of control synthesis for dynamical systems described by linear differential equations that function in accordance with the integral-quadratic quality criterion under uncertainty. External perturbations, errors and initial conditions belong to a certain set of uncertainties. Therefore, the problem of finding the optimal control in the form of feedback on the output of the object is presented in the form of a minimum problem of optimal control under uncertainty. The problem of finding the optimal control and initial state, which maximizes the quality criterion, is considered in the framework of the optimization problem, which is solved by the method of Lagrange multipliers after the introduction of the auxiliary scalar function - Hamiltonian. The case of a stationary system on an infinite period of time is considered. The formulas that can be used for calculations are given for the first and second variations. It is proposed to solve the problem of control search in two stages: search of intermediate solution at fixed values of control and error vectors and subsequent search of final optimal control. The solution of -optimal control for infinite time taking into account the signal from the compensator output is also considered, as well as the solution of the corresponding matrix algebraic equations of Ricatti type.
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Fahroo, Fariba. "Optimal Control." Journal of Guidance, Control, and Dynamics 24, no. 5 (September 2001): 1054–55. http://dx.doi.org/10.2514/2.4822.

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Naidu, D. "Optimal control." IEEE Transactions on Automatic Control 32, no. 10 (October 1987): 944. http://dx.doi.org/10.1109/tac.1987.1104454.

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Sargent, R. W. H. "Optimal control." Journal of Computational and Applied Mathematics 124, no. 1-2 (December 2000): 361–71. http://dx.doi.org/10.1016/s0377-0427(00)00418-0.

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Piccoli, Benedetto. "Optimal control." Automatica 38, no. 8 (August 2002): 1433–34. http://dx.doi.org/10.1016/s0005-1098(02)00022-5.

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Kučera, V., and J. V. Outrata. "Optimal control." Automatica 24, no. 1 (January 1988): 109–10. http://dx.doi.org/10.1016/0005-1098(88)90015-5.

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Venkateswarlu, A. "Optimal control." Control Engineering Practice 4, no. 7 (July 1996): 1035–36. http://dx.doi.org/10.1016/0967-0661(96)88552-2.

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Dissertations / Theses on the topic "Optimal control"

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Maslovskaya, Sofya. "Inverse Optimal Control : theoretical study." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLY013/document.

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Cette thèse s'insère dans un projet plus vaste, dont le but est de s'attaquer aux fondements mathématiques du problème inverse en contrôle optimal afin de dégager une méthodologie générale utilisable en neurophysiologie. Les deux questions essentielles sont : (a) l'unicité d'un coût pour une synthèse optimale donnée (injectivité); (b) la reconstruction du coût à partir de la synthèse. Pour des classes de coût générales, le problème apparaît très difficile même avec une dynamique triviale. On a donc attaqué l'injectivité pour des classes de problèmes spéciales : avec un coût quadratique, la dynamique étant soit non-holonome, soit affine en le contrôle. Les résultats obtenus ont permis de traiter la reconstruction pour le problème linéaire-quadratique
This PhD thesis is part of a larger project, whose aim is to address the mathematical foundations of the inverse problem in optimal control in order to reach a general methodology usable in neurophysiology. The two key questions are : (a) the uniqueness of a cost for a given optimal synthesis (injectivity) ; (b) the reconstruction of the cost from the synthesis. For general classes of costs, the problem seems very difficult even with a trivial dynamics. Therefore, the injectivity question was treated for special classes of problems, namely, the problems with quadratic cost and a dynamics, which is either non-holonomic (sub-Riemannian geometry) or control-affine. Based on the obtained results, we propose a reconstruction algorithm for the linear-quadratic problem
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Maslovskaya, Sofya. "Inverse Optimal Control : theoretical study." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLY013.

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Cette thèse s'insère dans un projet plus vaste, dont le but est de s'attaquer aux fondements mathématiques du problème inverse en contrôle optimal afin de dégager une méthodologie générale utilisable en neurophysiologie. Les deux questions essentielles sont : (a) l'unicité d'un coût pour une synthèse optimale donnée (injectivité); (b) la reconstruction du coût à partir de la synthèse. Pour des classes de coût générales, le problème apparaît très difficile même avec une dynamique triviale. On a donc attaqué l'injectivité pour des classes de problèmes spéciales : avec un coût quadratique, la dynamique étant soit non-holonome, soit affine en le contrôle. Les résultats obtenus ont permis de traiter la reconstruction pour le problème linéaire-quadratique
This PhD thesis is part of a larger project, whose aim is to address the mathematical foundations of the inverse problem in optimal control in order to reach a general methodology usable in neurophysiology. The two key questions are : (a) the uniqueness of a cost for a given optimal synthesis (injectivity) ; (b) the reconstruction of the cost from the synthesis. For general classes of costs, the problem seems very difficult even with a trivial dynamics. Therefore, the injectivity question was treated for special classes of problems, namely, the problems with quadratic cost and a dynamics, which is either non-holonomic (sub-Riemannian geometry) or control-affine. Based on the obtained results, we propose a reconstruction algorithm for the linear-quadratic problem
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Berović, Daniel Philip. "Optimal hybrid control." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408766.

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Boucher, Randy. "Galerkin optimal control." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44526.

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Approved for public release; distribution is unlimited
A Galerkin-based family of numerical formulations is presented for solving nonlinear optimal control problems. This dissertation introduces a family of direct methods that calculate optimal trajectories by discretizing the system dy-namics using Galerkin numerical techniques and approximate the cost function with Gaussian quadrature. In this numerical approach, the analysis is based on L2-norms. An important result in the theoretical foundation is that the feasibility and consistency theorems are proved for problems with continuous and/or piecewise continuous controls. Galerkin methods may be formulated in a number of ways that allow for efficiency and/or improved accuracy while solving a wide range of optimal control problems with a variety of state and control constraints. Numerical formula-tions using Lagrangian and Legendre test functions are derived. One formulation allows for a weak enforcement of boundary conditions, which imposes end conditions only up to the accuracy of the numerical approximation itself. Additionally, the multi-scale formulation can reduce the dimension of multi-scale optimal control problems, those in which the states and controls evolve on different timescales. Finally, numerical examples are shown to demonstrate the versatile nature of Galerkin optimal control.
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Shao, Cheng. "Biologically-inspired optimal control." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3102.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Mechanical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Stötzner, Ailyn. "Optimal Control of Thermoviscoplasticity." Universitätsverlag der Technischen Universität Chemnitz, 2018. https://monarch.qucosa.de/id/qucosa%3A31887.

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This thesis is devoted to the study of optimal control problems governed by a quasistatic, thermoviscoplastic model at small strains with linear kinematic hardening, von Mises yield condition and mixed boundary conditions. Mathematically, the thermoviscoplastic equations are given by nonlinear partial differential equations and a variational inequality of second kind in order to represent the elastic, plastic and thermal effects. Taking into account thermal effects we have to handle numerous mathematical challenges during the analysis of the thermoviscoplastic model, mainly due to the low integrability of the nonlinear terms on the right-hand side of the heat equation. One of our main results is the existence of a unique weak solution, which is proved by means of a fixed-point argument and by employing maximal parabolic regularity theory. Furthermore, we define the related control-to-state mapping and investigate properties of this mapping such as boundedness, weak continuity and local Lipschitz continuity. Another major result is the finding that the mapping is Hadamard differentiable; a main ingredient is the reformulation of the variational inequality, the so called viscoplastic flow rule, as a Banach space-valued ordinary differential equation with non-differentiable right-hand side. Subsequently, we consider an optimal control problem governed by thermoviscoplasticity and show the existence of a minimizer. Finally, close this thesis with numerical examples.
Diese Arbeit ist der Untersuchung von Optimalsteuerproblemen gewidmet, denen ein quasistatisches, thermoviskoplastisches Model mit kleinen Deformationen, mit linearem kinematischen Hardening, von Mises Fließbedingung und gemischten Randbedingungen zu Grunde liegt. Mathematisch werden thermoviskoplastische Systeme durch nichtlineare partielle Differentialgleichungen und eine variationelle Ungleichung der zweiten Art beschrieben, um die elastischen, plastischen und thermischen Effekte abzubilden. Durch die Miteinbeziehung thermischer Effekte, treten verschiedene mathematische Schwierigkeiten während der Analysis des thermoviskoplastischen Systems auf, die ihren Ursprung hauptsächlich in der schlechten Regularität der nichtlinearen Terme auf der rechten Seite der Wärmeleitungsgleichung haben. Eines unserer Hauptresultate ist die Existenz einer eindeutigen schwachen Lösung, welches wir mit Hilfe von einem Fixpunktargument und unter Anwendung von maximaler parabolischer Regularitätstheorie beweisen. Zudem definieren wir die entsprechende Steuerungs-Zustands-Abbildung und untersuchen Eigenschaften dieser Abbildung wie die Beschränktheit, schwache Stetigkeit und lokale Lipschitz Stetigkeit. Ein weiteres wichtiges Resultat ist, dass die Abbildung Hadamard differenzierbar ist; Hauptbestandteil des Beweises ist die Umformulierung der variationellen Ungleichung, der sogenannten viskoplastischen Fließregel, als eine Banachraum-wertige gewöhnliche Differentialgleichung mit nichtdifferenzierbarer rechter Seite. Schließlich runden wir diese Arbeit mit numerischen Beispielen ab.
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Al, Helal Zahra Hassan A. "Optimal control of diabetes." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/2107.

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This thesis considers optimal control problems related to one of the major global health problems, Diabetes. We adopt a comprehensive dynamic model of the blood glucose regulatory system and show how it can be readily fitted to individuals. Based on this, we develop a composite dynamic model for simulating the effects of exercise and subcutaneous insulin injections on the blood glucose regulatory system. We then determine that optimal treatment regimens on the basis of the composite model.
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Pfeiffer, Laurent. "Sensitivity analysis for optimal control problems. Stochastic optimal control with a probability constraint." Palaiseau, Ecole polytechnique, 2013. https://pastel.hal.science/docs/00/88/11/19/PDF/thesePfeiffer.pdf.

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Cette thèse est divisée en deux parties. Dans la première partie, nous étudions des problèmes de contrôle optimal déterministes avec contraintes et nous nous intéressons à des questions d'analyse de sensibilité. Le point de vue que nous adoptons est celui de l'optimisation abstraite; les conditions d'optimalité nécessaires et suffisantes du second ordre jouent alors un rôle crucial et sont également étudiées en tant que telles. Dans cette thèse, nous nous intéressons à des solutions fortes. De façon générale, nous employons ce terme générique pour désigner des contrôles localement optimaux pour la norme L1. En renforçant la notion d'optimalité locale utilisée, nous nous attendons à obtenir des résultats plus forts. Deux outils sont utilisés de façon essentielle : une technique de relaxation, qui consiste à utiliser plusieurs contrôles simultanément, ainsi qu'un principe de décomposition, qui est un développement de Taylor au second ordre particulier du lagrangien. Les chapitres 2 et 3 portent sur les conditions d'optimalité nécessaires et suffisantes du second ordre pour des solutions fortes de problèmes avec contraintes pures, mixtes et sur l'état final. Dans le chapitre 4, nous réalisons une analyse de sensibilité pour des problèmes relaxés avec des contraintes sur l'état final. Dans le chapitre 5, nous réalisons une analyse de sensibilité pour un problème de production d'énergie nucléaire. Dans la deuxième partie, nous étudions des problèmes de contrôle optimal stochastique sous contrainte en probabilité. Nous étudions une approche par programmation dynamique, dans laquelle le niveau de probabilité est vu comme une variable d'état supplémentaire. Dans ce cadre, nous montrons que la sensibilité de la fonction valeur par rapport au niveau de probabilité est constante le long des trajectoires optimales. Cette analyse nous permet de développer des méthodes numériques pour des problèmes en temps continu. Ces résultats sont présentés dans le chapitre 6, dans lequel nous étudions également une application à la gestion actif-passif
This thesis is divided into two parts. In the first part, we study constrained deterministic optimal control problems and sensitivity analysis issues, from the point of view of abstract optimization. Second-order necessary and sufficient optimality conditions, which play an important role in sensitivity analysis, are also investigated. In this thesis, we are interested in strong solutions. We use this generic term for locally optimal controls for the L1-norm, roughly speaking. We use two essential tools: a relaxation technique, which consists in using simultaneously several controls, and a decomposition principle, which is a particular second-order Taylor expansion of the Lagrangian. Chapters 2 and 3 deal with second-order necessary and sufficient optimality conditions for strong solutions of problems with pure, mixed, and final-state constraints. In Chapter 4, we perform a sensitivity analysis for strong solutions of relaxed problems with final-state constraints. In Chapter 5, we perform a sensitivity analysis for a problem of nuclear energy production. In the second part of the thesis, we study stochastic optimal control problems with a probability constraint. We study an approach by dynamic programming, in which the level of probability is a supplementary state variable. In this framework, we show that the sensitivity of the value function with respect to the probability level is constant along optimal trajectories. We use this analysis to design numerical schemes for continuous-time problems. These results are presented in Chapter 6, in which we also study an application to asset-liability management
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Vanichsriratana, Wirat. "Optimal control of fed-batch fermentation processes." Thesis, University of Westminster, 1996. https://westminsterresearch.westminster.ac.uk/item/94908/optimal-control-of-fed-batch-fermentation-processes.

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Optimisation of a fed-batch fermentation process typically uses the calculus of variations or Pontryagin's maximum principle to determine an optimal feed rate profile. This often results in a singular control problem and an open loop control structure. The singular feed rate is the optimal feed rate during the singular control period and is used to control the substrate concentration in the fermenter at an optimal level. This approach is supported by biological knowledge that biochemical reaction rates are controlled by the environmental conditions in the fermenter; in this case, the substrate concentration. Since an accurate neural net-based on-line estimation of the substrate concentration has recently become available and is currently employed in industry, we are therefore able to propose a method which makes use of this estimation. The proposed method divides the optimisation problem into two parts. First, an optimal substrate concentration profile which governs the biochemical reactions in the fermentation process is determined. Then a controller is designed to track the obtained optimal profile. Since the proposed method determines the optimal substrate concentration profile, the singular control problem is therefore avoided because the substrate concentration appears nonlinearly in the system equations. Also, the process is then operated in closed loop control of the substrate concentration. The proposed method is then called "closed loop optimal control". The proposed closed loop optimal control method is then compared with the open loop optimal feed rate profile method. The comparison simulations from both primary and secondary metabolite production processes show that both methods give similar performance in a case of perfect model while the closed loop optimal control provides better performance than the open loop method in a case of plant/model mismatch. The better performance of the closed loop optimal control is due to an ability to compensate for the modelling errors using feedback.
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Carlsson, Jesper. "Optimal Control of Partial Differential Equations in Optimal Design." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9293.

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This thesis concerns the approximation of optimally controlled partial differential equations for inverse problems in optimal design. Important examples of such problems are optimal material design and parameter reconstruction. In optimal material design the goal is to construct a material that meets some optimality criterion, e.g. to design a beam, with fixed weight, that is as stiff as possible. Parameter reconstrucion concerns, for example, the problem to find the interior structure of a material from surface displacement measurements resulting from applied external forces. Optimal control problems, particularly for partial differential equations, are often ill-posed and need to be regularized to obtain good approximations. We here use the theory of the corresponding Hamilton-Jacobi-Bellman equations to construct regularizations and derive error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method where the first, analytical, step is to regularize the Hamiltonian. Next its Hamiltonian system is computed efficiently with the Newton method using a sparse Jacobian. An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its L² projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems. Another treated issue is the relevance of input data for parameter reconstruction problems, where the goal is to determine a spacially distributed coefficient of a partial differential equation from partial observations of the solution. It is here shown that the choice of input data, that generates the partial observations, affects the reconstruction, and that it is possible to formulate meaningful optimality criteria for the input data that enhances the quality of the reconstructed coefficient. In the thesis we present solutions to various applications in optimal material design and reconstruction.
Denna avhandling handlar om approximation av optimalt styrda partiella differentialekvationer för inversa problem inom optimal design. Viktiga exempel på sådana problem är optimal materialdesign och parameterskattning. Inom materialdesign är målet att konstruera ett material som uppfyller vissa optimalitetsvillkor, t.ex. att konstruera en så styv balk som möjligt under en given vikt, medan ett exempel på parameterskattning är att hitta den inre strukturen hos ett material genom att applicera ytkrafter och mäta de resulterande förskjutningarna. Problem inom optimal styrning, speciellt för styrning av partiella differentialekvationer,är ofta illa ställa och måste regulariseras för att kunna lösas numeriskt. Teorin för Hamilton-Jacobi-Bellmans ekvationer används här för att konstruera regulariseringar och ge feluppskattningar till problem inom optimaldesign. Den konstruerade Pontryaginmetoden är en enkel och generell metod där det första analytiska steget är att regularisera Hamiltonianen. I nästa steg löses det Hamiltonska systemet effektivt med Newtons metod och en gles Jacobian. Vi härleder även en feluppskattning för skillnaden mellan den exakta och den approximerade målfunktionen. Denna uppskattning beror endast på skillnaden mellan den sanna och den regulariserade, ändligt dimensionella, Hamiltonianen, båda utvärderade längst lösningsbanan och dessL²-projektion. Felet beror alltså ej på skillnaden mellan den exakta och denapproximativa lösningen till det Hamiltonska systemet. Ett annat fall som behandlas är frågan hur indata ska väljas för parameterskattningsproblem. För sådana problem är målet vanligen att bestämma en rumsligt beroende koefficient till en partiell differentialekvation, givet ofullständiga mätningar av lösningen. Här visas att valet av indata, som genererarde ofullständiga mätningarna, påverkar parameterskattningen, och att det är möjligt att formulera meningsfulla optimalitetsvillkor för indata som ökar kvaliteten på parameterskattningen. I avhandlingen presenteras lösningar för diverse tillämpningar inom optimal materialdesign och parameterskattning.
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Books on the topic "Optimal control"

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Lewis, Frank L. Optimal control. 3rd ed. Hoboken: Wiley, 2012.

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L, Syrmos Vassilis, ed. Optimal control. 2nd ed. New York: Wiley, 1995.

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Ashchepkov, Leonid T., Dmitriy V. Dolgy, Taekyun Kim, and Ravi P. Agarwal. Optimal Control. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91029-7.

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Alekseev, V. M., V. M. Tikhomirov, and S. V. Fomin. Optimal Control. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-7551-1.

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Lewis, Frank L., Draguna L. Vrabie, and Vassilis L. Syrmos. Optimal Control. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118122631.

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Locatelli, Arturo. Optimal Control. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8328-3.

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Bulirsch, R., A. Miele, J. Stoer, and K. Well, eds. Optimal Control. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-7539-4.

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Bulirsch, Roland, Angelo Miele, Josef Stoer, and Klaus H. Well, eds. Optimal Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0040194.

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Vinter, Richard. Optimal Control. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-8086-2.

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Aschepkov, Leonid T., Dmitriy V. Dolgy, Taekyun Kim, and Ravi P. Agarwal. Optimal Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49781-5.

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Book chapters on the topic "Optimal control"

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Corriou, Jean-Pierre. "Optimal Control." In Process Control, 539–609. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_14.

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Corriou, Jean-Pierre. "Optimal Control." In Process Control, 493–554. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_14.

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Polak, Elijah. "Optimal Control." In Applied Mathematical Sciences, 482–645. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0663-7_4.

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Andrei, Neculai. "Optimal Control." In Nonlinear Optimization Applications Using the GAMS Technology, 287–322. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6797-7_12.

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Bloch, A. M. "Optimal Control." In Nonholonomic Mechanics and Control, 329–66. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97376_7.

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Marin, Marin, and Andreas Öchsner. "Optimal Control." In Complements of Higher Mathematics, 319–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74684-5_10.

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Preumont, André. "Optimal control." In Vibration Control of Active Structures, 145–72. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5654-7_8.

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Kulkarni, V. G. "Optimal Control." In Modeling, Analysis, Design, and Control of Stochastic Systems, 317–51. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3098-2_10.

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Eriksson, Kenneth, Claes Johnson, and Donald Estep. "Optimal Control." In Applied Mathematics: Body and Soul, 1093–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05800-8_26.

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Hu, Shouchuan, and Nikolas S. Papageorgiou. "Optimal Control." In Handbook of Multivalued Analysis, 351–508. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4665-8_4.

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Conference papers on the topic "Optimal control"

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Whidborne, James F. "Solving optimal control problems using chebfun." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737522.

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Nie, Yuanbo, and Eric C. Kerrigan. "Capturing Discontinuities in Optimal Control Problems." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516770.

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Ye, Lingjian, and Yi Cao. "A formulation for globally optimal controlled variable selection." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334619.

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Memon, Attaullah Y. "Optimal output regulation of minimum phase nonlinear systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334679.

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Khelassi, Abdelmadjid, Riad Bendib, and Abdelhai Benhalla. "Configurations of binary distillation column for optimal control." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334731.

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Kablar, Natasa A., and Vlada Kvrgic. "Inverse optimal robust control of singularly impulsive dynamical systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334667.

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Hasan, S. N., and J. A. Rossiter. "Free flight concept formulation exploiting neighbouring Optimal Control concepts." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334699.

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Miguel Ferreira, Joao. "Optimal Control of Rodent Populations." In 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO). IEEE, 2018. http://dx.doi.org/10.1109/controlo.2018.8514260.

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Genest, Romain, and John Ringwood. "Receding horizon pseudospectral optimal control for wave energy conversion." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737513.

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Ling, Weifang, Minyou Chen, Zuolin Wei, Feixiong Chen, Lei Yu, and David C. Yu. "A distributed optimal control method for active distribution network." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737568.

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Reports on the topic "Optimal control"

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Rabitz, Herschel. Optimal Control of MoIecular Motion. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada291919.

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Chen, Yan, Arnab Bhattacharya, Jing Li, and Draguna Vrabie. Optimal Control by Transfer-Learning. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1988297.

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Shreve, S. E., and V. J. Mizel. Optimal Control with Diminishing and Zero Cost for Control. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada182805.

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Shao, Cheng, and Dimitrios Hristu-Varsakelis. Optimal Control through Biologically-Inspired Pursuit. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada439266.

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Shao, Cheng, and Dimitrios Hristu-Varsakelis. Biologically Inspired Algorithms for Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada439518.

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von Winckel, Gregory John. Optimal Design and Control of Qubits. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475100.

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Ray, Asok, and Travis Ortogero. Language Measure for Robust Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444858.

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Sachs, Ekkehard W. Superlinear Convergent Algorithms in Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada179614.

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Desbrun, Mathieu, and Marin Kobilarov. Geometric Computational Mechanics and Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada564028.

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Shao, Cheng, and D. Hristu-Varsakelis. Biologically-Inspired Optimal Control via Intermittent Cooperation. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada438963.

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