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Sept. 13, 2025, 11:05 a.m.
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(Last updated: Sept. 13, 2025, 11:08 a.m.)
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| Meurer T. Optimal and Model Predictive Control 2025.pdf | 1.5 MB |
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SOURCE: Meurer T. Optimal and Model Predictive Control 2025
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MEDIAINFO
Textbook in PDF format Problems of decision making in physical and technical systems, economics or organizations have received increasing attention mainly inspired by the benefits which may result from a proper (optimal in some sense) decision concerning the distribution of expensive resources. This development is further fostered by the evolution of computing and computational hardware enabling an efficient and fast solution of also highly complex decision problems. The concept of optimal decisions has emerged as the fundamental approach for the formulation of decision problems (Luenberger, 1969). Herein a single quantity describing performance or value is either minimized or maximized by proper selection among the available alternatives. The resulting optimal decision is taken as the solution of the decision problem. For the determination of an optimal decision or optimal solution, respectively, it is necessary to provide a mathematical formulation of the decision or optimization problem (OP). It is in general distinguished between static optimization problems addressing the minimization of a function with optimization variables (also called decision variables) from the Euclidean space and dynamic optimization problems with the decision variables being elements of an infinite–dimensional function space, e.g. functions of time. Some properties and differences between static and dynamic optimization problems are presented in this introductory section based on selective example problems. In addition, mathematical concepts are addressed, which are used throughout the text
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