An Approach for Achieving Self-Optimization in Mechatronic Systems Supported by Active Patterns

Abstract

The conceivable development of information technology will enable mechatronic systems with inherent partial intelligence. We refer to this by using the term "self-optimization". Self-optimizing systems react autonomously and flexibly on changing environmental conditions. They learn and optimize their behavior during operation. The design of self-optimizing mechatronic systems is an interdisciplinary task: Experts in mechanical and electrical engineering develop hardware components while mathematicians and computer scientists bring intelligence to these components. Successful realization of the latter aspect is achieved by using a combination of modern modeling techniques and algorithmic methods. In this paper, we present a new approach which enables mechatronic systems to adapt their objectives to changing environment during operation. Furthermore, we explain the use of solution patterns in order to enable the reuse of proven solutions, which provide self-optimization in mechatronic systems.