Abstract
Machines are ubiquitous. They produce, they transport. Machines facilitate and assist with work. The increasing fusion of mechanical engineering with information technology has brought about considerable benefits. This situation is expressed by the term mechatronics, which means the close interaction of mechanics, electrics/electronics, control engineering and software engineering to improve the behavior of a technical system. The integration of cognitive functions into mechatronic systems enables systems to have inherent partial intelligence. The behavior of these future systems is formed by the communication and cooperation of the intelligent system elements. From an information processing point of view, we consider these distributed systems to be multi-agent-systems. These capabilities open up fascinating prospects regarding the design of future technical systems. The term self-optimization characterizes this perspective: the endogenous adaptation of the system’s objectives due to changing operational conditions. This resuls in an autonomous adjustment of system parameters or system structure and consequently of the system’s behavior. In this chapter self-optimizing systems are described in detail. The long term aim of the Collaborative Research Centre 614 ”Self-Optimizing Concepts and Structures in Mechanical Engineering” is to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop these systems. For this, developers have to face a number of challenges, e.g. the multidisciplinarity and the complexity of the system. This book povides a design methodology that helps to master these challenges and to enable third parties to develop self-optimizing systems by themselves.
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Keywords
- Optimal Control Problem
- Multiobjective Optimization
- Technical System
- Multiobjective Optimization Problem
- Mechatronic System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Dellnitz, M. et al. (2014). The Paradigm of Self-optimization. In: Gausemeier, J., Rammig, F., Schäfer, W. (eds) Design Methodology for Intelligent Technical Systems. Lecture Notes in Mechanical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45435-6_1
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DOI: https://doi.org/10.1007/978-3-642-45435-6_1
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