Self-optimization is the ability of a mechatronic system to autonomously adapt the way it performs its functions to changing environmental and operational conditions or user demands. In this work, we consider the self-optimization of the guidance of a rail-bound vehicle. Here, four objective functions are destined to be minimized at the same time. This leads to a multiobjective optimal control problem. As the guidance module belongs to the class of differentially flat systems, which are characterized by the fact that the inputs and states can be represented as a function of the flat outputs and their derivatives, the control problem can be converted into a multiobjective optimization problem with constraints. The solution to this problem is the set of optimal compromises of the objectives, the socalled Pareto set, which we approximate (offline) by using a set-oriented numerical method. Bearing in mind this entire information we choose a set of satisfactory trajectories which fit to different situations such as wet or dry rails, abrasion, energy supply, etc. The corresponding weighting of the objectives is stored and then a model-predictive control process can be solved online that situationally optimizes the weighted sum of the objectives.
Self-optimization of the guidance module of a rail-bound vehicle
Self-Optimizing Mechatronic Systems: Design the Future, International Heinz Nixdorf Symposium, 7 ; 85-100
2008
16 Seiten, 9 Bilder, 16 Quellen
Aufsatz (Konferenz)
Englisch