Session:

Hybrid System Applications
Sunday, February 29, 2004, 12.40 – 13.00

Session Chair:

Fikret Gürgen

Paper Title:

On Learning from past experience as a Meta-Methodology for the Application on Self-Optimizing working Principles within Complex Hierarchical Mechatronic Systems

Author(s):

A. Schmidt, Heinz Nixdorf Institute, University of Paderborn, Germany
P. Scheideler, Heinz Nixdorf Institute, University of Paderborn, Germany

Abstract:

Modern mechatronic products make use of the close interaction between mechanics, electronics, control engineering and software. As those systems increase in complexity and the interaction with its environment demands autonomous behavior, the need for inherent intelligence becomes ever urgent. Based on a framework for self-optimizing mechatronic systems, this paper introduces Working Principles of Self-Optimization as intelligent building blocks for self-optimizing module-agents. As a consequence of changing influences on the technical system, the working principles allow for an endogenous modification of the module-agents’ multi-objective system and for an autonomous adaptation of its parameterization, behavior and structure. Knowledge bases of working principles together with an extended process model for learning from past experience enables the mechatronic agents to apply stored working principles to current situations and to learn from the outcome of their execution. The proposed approach is verified within a railway application scenario. The linear-motor drive of shuttle trains is self-optimized by the working principle of Preview Control.