Literatura académica sobre el tema "Deterministic optimal control"
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Artículos de revistas sobre el tema "Deterministic optimal control"
Chaplais, F. "Averaging and Deterministic Optimal Control". SIAM Journal on Control and Optimization 25, n.º 3 (mayo de 1987): 767–80. http://dx.doi.org/10.1137/0325044.
Texto completoBehncke, Horst. "Optimal control of deterministic epidemics". Optimal Control Applications and Methods 21, n.º 6 (noviembre de 2000): 269–85. http://dx.doi.org/10.1002/oca.678.
Texto completoPareigis, Stephan. "Learning optimal control in deterministic systems". ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 78, S3 (1998): 1033–34. http://dx.doi.org/10.1002/zamm.19980781585.
Texto completoWang, Yuanchang y Jiongmin Yong. "A deterministic affine-quadratic optimal control problem". ESAIM: Control, Optimisation and Calculus of Variations 20, n.º 3 (21 de mayo de 2014): 633–61. http://dx.doi.org/10.1051/cocv/2013078.
Texto completoVerms, D. "Optimal control of piecewise deterministic markov process". Stochastics 14, n.º 3 (febrero de 1985): 165–207. http://dx.doi.org/10.1080/17442508508833338.
Texto completoSoravia, Pierpaolo. "On Aronsson Equation and Deterministic Optimal Control". Applied Mathematics and Optimization 59, n.º 2 (28 de mayo de 2008): 175–201. http://dx.doi.org/10.1007/s00245-008-9048-7.
Texto completoHaurie, A., A. Leizarowitz y Ch van Delft. "Boundedly optimal control of piecewise deterministic systems". European Journal of Operational Research 73, n.º 2 (marzo de 1994): 237–51. http://dx.doi.org/10.1016/0377-2217(94)90262-3.
Texto completoSeierstad, Atle. "Existence of optimal nonanticipating controls in piecewise deterministic control problems". ESAIM: Control, Optimisation and Calculus of Variations 19, n.º 1 (18 de enero de 2012): 43–62. http://dx.doi.org/10.1051/cocv/2011197.
Texto completoMitsos, Alexander, Jaromił Najman y Ioannis G. Kevrekidis. "Optimal deterministic algorithm generation". Journal of Global Optimization 71, n.º 4 (13 de febrero de 2018): 891–913. http://dx.doi.org/10.1007/s10898-018-0611-8.
Texto completoYu, Juanyi, Jr-Shin Li y Tzyh-Jong Tarn. "Optimal Control of Gene Mutation in DNA Replication". Journal of Biomedicine and Biotechnology 2012 (2012): 1–26. http://dx.doi.org/10.1155/2012/743172.
Texto completoTesis sobre el tema "Deterministic optimal control"
Ribeiro, do Val Joao Bosco. "Stochastic optimal control for piecewise deterministic Markov processes". Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38142.
Texto completoJohnson, Miles J. "Inverse optimal control for deterministic continuous-time nonlinear systems". Thesis, University of Illinois at Urbana-Champaign, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3632073.
Texto completoInverse optimal control is the problem of computing a cost function with respect to which observed state input trajectories are optimal. We present a new method of inverse optimal control based on minimizing the extent to which observed trajectories violate first-order necessary conditions for optimality. We consider continuous-time deterministic optimal control systems with a cost function that is a linear combination of known basis functions. We compare our approach with three prior methods of inverse optimal control. We demonstrate the performance of these methods by performing simulation experiments using a collection of nominal system models. We compare the robustness of these methods by analyzing how they perform under perturbations to the system. We consider two scenarios: one in which we exactly know the set of basis functions in the cost function, and another in which the true cost function contains an unknown perturbation. Results from simulation experiments show that our new method is computationally efficient relative to prior methods, performs similarly to prior approaches under large perturbations to the system, and better learns the true cost function under small perturbations. We then apply our method to three problems of interest in robotics. First, we apply inverse optimal control to learn the physical properties of an elastic rod. Second, we apply inverse optimal control to learn models of human walking paths. These models of human locomotion enable automation of mobile robots moving in a shared space with humans, and enable motion prediction of walking humans given partial trajectory observations. Finally, we apply inverse optimal control to develop a new method of learning from demonstration for quadrotor dynamic maneuvering. We compare and contrast our method with an existing state-of-the-art solution based on minimum-time optimal control, and show that our method can generalize to novel tasks and reject environmental disturbances.
Laera, Simone. "VWAP OPTIMAL EXECUTION Deterministic and stochastic approaches". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Buscar texto completoCosta, Oswaldo Luiz de Valle. "Approximations for optimal stopping and impulsive control of piecewise-deterministic processes". Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38271.
Texto completoLange, Dirk Klaus [Verfasser] y N. [Akademischer Betreuer] Bäuerle. "Cost optimal control of Piecewise Deterministic Markov Processes under partial observation / Dirk Klaus Lange ; Betreuer: N. Bäuerle". Karlsruhe : KIT-Bibliothek, 2017. http://d-nb.info/1132997739/34.
Texto completoSainvil, Watson. "Contrôle optimal et application aux énergies renouvelables". Electronic Thesis or Diss., Antilles, 2023. http://www.theses.fr/2023ANTI0894.
Texto completoToday, electricity is the easiest form of energy to exploit in the world. However, producing it from fossil sources such as oil, coal, natural gas,…, is the main cause of global warming by emitting a massive amount of greenhouse gases into nature. We need an alternative and fast! The almost daily sunshine and the important quantity of wind should favor the development of renewable energies.In this thesis, the main objective is to apply the optimal control theory to renewable energies in order to convince decision makers to switch to them through mathematical studies. First, we develop a deterministic case based on what has already been done in the transition from fossil fuels to renewable energies in which we formulate two case studies. The first one deals with an optimal control probleminvolving the transition from oil to solar energy. The second deals with an optimal control problem involving the transition from oil to solar and wind energies.Then, we develop a stochastic part in which we treat a stochastic control problem whose objective is to take into account the random aspect of the production of solar energy since we cannot guarantee sufficient daily sunshine
Schlosser, Rainer. "Six essays on stochastic and deterministic dynamic pricing and advertising models". Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2014. http://dx.doi.org/10.18452/16973.
Texto completoThe cumulative dissertation deals with stochastic and deterministic dynamic sales models for durable as well as perishable products. The models analyzed are characterized by simultaneous dynamic pricing and advertising controls in continuous time and are in line with recent developments in dynamic pricing. They include the modeling of multi-dimensional decisions and take (i) time dependencies, (ii) adoption effects (iii), competitive settings and (iv) risk aversion, explicitly into account. For special cases with isoelastic demand functions as well as with exponential ones explicit solution formulas of the optimal pricing and advertising feedback controls are derived. Moreover, optimally controlled sales processes are analytically described. In particular, the distribution of profits, the expected evolution of prices as well as inventory levels are analyzed in detail and sensitivity results are obtained. Furthermore, we consider the question whether or not monopolistic policies are socially efficient; in special cases, we propose taxation/subsidy mechanisms to establish efficiency. The results are presented in six articles and provide economic insights into a variety of dynamic sales applications of the business world, especially in the area of e-commerce.
Tan, Yang. "Optimal Discrete-in-Time Inventory Control of a Single Deteriorating Product with Partial Backlogging". Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3711.
Texto completoJoubaud, Maud. "Processus de Markov déterministes par morceaux branchants et problème d’arrêt optimal, application à la division cellulaire". Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS031/document.
Texto completoPiecewise deterministic Markov processes (PDMP) form a large class of stochastic processes characterized by a deterministic evolution between random jumps. They fall into the class of hybrid processes with a discrete mode and an Euclidean component (called the state variable). Between the jumps, the continuous component evolves deterministically, then a jump occurs and a Markov kernel selects the new value of the discrete and continuous components. In this thesis, we extend the construction of PDMPs to state variables taking values in some measure spaces with infinite dimension. The aim is to model cells populations keeping track of the information about each cell. We study our measured-valued PDMP and we show their Markov property. With thoses processes, we study a optimal stopping problem. The goal of an optimal stopping problem is to find the best admissible stopping time in order to optimize some function of our process. We show that the value fonction can be recursively constructed using dynamic programming equations. We construct some $epsilon$-optimal stopping times for our optimal stopping problem. Then, we study a simple finite-dimension real-valued PDMP, the TCP process. We use Euler scheme to approximate it, and we estimate some types of errors. We illustrate the results with numerical simulations
Geeraert, Alizée. "Contrôle optimal stochastique des processus de Markov déterministes par morceaux et application à l’optimisation de maintenance". Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0602/document.
Texto completoWe are interested in a discounted impulse control problem with infinite horizon forpiecewise deterministic Markov processes (PDMPs). In the first part, we model the evolutionof an optronic system by PDMPs. To optimize the maintenance of this equipment, we study animpulse control problem where both maintenance costs and the unavailability cost for the clientare considered. We next apply a numerical method for the approximation of the value function associated with the impulse control problem, which relies on quantization of PDMPs. The influence of the parameters on the numerical results is discussed. In the second part, we extendthe theoretical study of the impulse control problem by explicitly building a family of є-optimalstrategies. This approach is based on the iteration of a single-jump-or-intervention operator associatedto the PDMP and relies on the theory for optimal stopping of a piecewise-deterministic Markov process by U.S. Gugerli. In the present situation, the main difficulty consists in approximating the best position after the interventions, which is done by introducing a new operator.The originality of the proposed approach is the construction of є-optimal strategies that areexplicit, since they do not require preliminary resolutions of complex problems
Libros sobre el tema "Deterministic optimal control"
Jadamba, Baasansuren, Akhtar A. Khan, Stanisław Migórski y Miguel Sama. Deterministic and Stochastic Optimal Control and Inverse Problems. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003050575.
Texto completoCarlson, D. A. Infinite horizon optimal control: Deterministic and stochastic systems. 2a ed. Berlin: Springer-Verlag, 1991.
Buscar texto completoCarlson, Dean A. Infinite Horizon Optimal Control: Deterministic and Stochastic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.
Buscar texto completoMordukhovich, Boris S. y Hector J. Sussmann, eds. Nonsmooth Analysis and Geometric Methods in Deterministic Optimal Control. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4613-8489-2.
Texto completoKolosov, G. E. Optimal design of control systems: Stochastic and deterministic problems. New York: M. Dekker, 1999.
Buscar texto completoSh, Mordukhovich B. y Sussmann Hector J. 1946-, eds. Nonsmooth analysis and geometric methods in deterministic optimal control. New York: Springer, 1996.
Buscar texto completoFleming, Wendell H. Deterministic and Stochastic Optimal Control. Springer, 2012.
Buscar texto completoFleming, Wendell H. y Raymond W. Rishel. Deterministic and Stochastic Optimal Control. Springer London, Limited, 2012.
Buscar texto completoMoyer, H. Gardner. Deterministic Optimal Control: An Introduction for Scientists. Trafford Publishing, 2006.
Buscar texto completoKhan, Akhtar A., Baasansuren Jadamba, Stanislaw Migorski y Miguel Angel Sama Meige. Deterministic and Stochastic Optimal Control and Inverse Problems. Taylor & Francis Group, 2021.
Buscar texto completoCapítulos de libros sobre el tema "Deterministic optimal control"
Bensoussan, Alain. "Deterministic Optimal Control". En Interdisciplinary Applied Mathematics, 215–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75456-7_10.
Texto completoSeierstad, Atle. "Piecewise Deterministic Optimal Control Problems". En Stochastic Control in Discrete and Continuous Time, 1–70. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76617-1_3.
Texto completode Saporta, Benoîte, François Dufour y Huilong Zhang. "Optimal Impulse Control". En Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes, 231–67. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119145066.ch10.
Texto completoDontchev, A. L. "Discrete Approximations in Optimal Control". En Nonsmooth Analysis and Geometric Methods in Deterministic Optimal Control, 59–80. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4613-8489-2_3.
Texto completoZoppoli, Riccardo, Marcello Sanguineti, Giorgio Gnecco y Thomas Parisini. "Deterministic Optimal Control over a Finite Horizon". En Neural Approximations for Optimal Control and Decision, 255–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29693-3_6.
Texto completoCosta, O. L. V. y F. Dufour. "Optimal Control of Piecewise Deterministic Markov Processes". En Stochastic Analysis, Filtering, and Stochastic Optimization, 53–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98519-6_3.
Texto completoBressan, Alberto. "Impulsive Control Systems". En Nonsmooth Analysis and Geometric Methods in Deterministic Optimal Control, 1–22. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4613-8489-2_1.
Texto completoFilatova, Darya. "Optimal Control Strategies for Stochastic/Deterministic Bioeconomic Models". En Mathematics in Industry, 537–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25100-9_62.
Texto completoChen, Lijun, Na Li, Libin Jiang y Steven H. Low. "Optimal Demand Response: Problem Formulation and Deterministic Case". En Control and Optimization Methods for Electric Smart Grids, 63–85. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1605-0_3.
Texto completoZolezzi, Tullio. "Well Posed Optimal Control Problems: A Perturbation Approach". En Nonsmooth Analysis and Geometric Methods in Deterministic Optimal Control, 239–46. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4613-8489-2_11.
Texto completoActas de conferencias sobre el tema "Deterministic optimal control"
Zamani, Mohammad, Jochen Trumpf y Robert Mahony. "Near-optimal deterministic attitude filtering". En 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717043.
Texto completoLi, Yuchao, Karl H. Johansson, Jonas Martensson y Dimitri P. Bertsekas. "Data-driven Rollout for Deterministic Optimal Control". En 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683499.
Texto completoBarles, G. y B. Perthame. "Discontinuous viscosity solutions of deterministic optimal control problems". En 1986 25th IEEE Conference on Decision and Control. IEEE, 1986. http://dx.doi.org/10.1109/cdc.1986.267221.
Texto completoCoote, Paul, Jochen Trumpf, Robert Mahony y Jan C. Willems. "Near-optimal deterministic filtering on the unit circle". En 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5399999.
Texto completoLiao, Y. y S. Lenhart. "Optimal control of piecewise-deterministic processes with discrete control actions". En 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268634.
Texto completoBasin, Michael V. y Irma R. Valadez Guzman. "Optimal controller for integral Volterra systems with deterministic uncertainties". En 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7075973.
Texto completoBasin, Michael y Dario Calderon-Alvarez. "Optimal controller for uncertain stochastic polynomial systems with deterministic disturbances". En 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160068.
Texto completoTsumura, Koji. "Optimal Quantizer for Mixed Probabilistic/Deterministic Parameter Estimation". En Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.376940.
Texto completo"COST-OPTIMAL STRONG PLANNING IN NON-DETERMINISTIC DOMAINS". En 8th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003448200560066.
Texto completoSun, Jin-gen, Li-jun Fu, Zhi-gang Huang y Dong-sheng Wu. "The Method of Deterministic Optimal Control with Box Constraints". En 2010 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2010. http://dx.doi.org/10.1109/icinis.2010.37.
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