Determination of Active Pattern during the Conceptual Design of Self-Optimizing Systems Demonstrated by an Air Gap Adjustment System

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 systems is an interdisciplinary task. Mechanical, electrical, control, and software engineers are involved as well as experts from mathematical optimization and artificial intelligence. During the phase "conceptual design" developers have to choose solution patterns for functions from various domains. Different terminologies complicate the search for appropiate solution patterns, espacially in case of solution patterns used for self- optimization. In this contribution we introduce the active pattern for self-optimization as a new type of solution patterns. We present a domain spanning search for active patterns for self-optimization and demonstrate the search at a self-optimizing air gap adjustment system of a linear drive.