Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control [electronic resource] / by Christian Kirches.
By: Kirches, Christian [author.].
Contributor(s): SpringerLink (Online service).
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BookPublisher: Wiesbaden : Vieweg+Teubner Verlag, 2011Description: XX, 367p. 64 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783834882028.Subject(s): Computer science | Computer simulation | Mathematics | Computer Science | Simulation and Modeling | Mathematics, generalDDC classification: 003.3 Online resources: Click here to access online
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Springer eBooksSummary: Current industrial practice knows many optimization tasks that can be cast as mixed-integer optimal control problems. Due to the combinatorial character of these problems, the computation of optimal solutions under real-time constraints is still a demanding challenge. Starting with Bock's direct multiple shooting method for optimal control, Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding. In a sequential quadratic programming framework, extensive exploitation of arising structures in an active set method ultimately brings the developed algorithm towards real-time feasibility.
Current industrial practice knows many optimization tasks that can be cast as mixed-integer optimal control problems. Due to the combinatorial character of these problems, the computation of optimal solutions under real-time constraints is still a demanding challenge. Starting with Bock's direct multiple shooting method for optimal control, Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding. In a sequential quadratic programming framework, extensive exploitation of arising structures in an active set method ultimately brings the developed algorithm towards real-time feasibility.
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