17. Tagung - Der Arbeitsprozess des Verbrennungsmotors
Computation time optimization of a model-based predictive roll stabilization by neuro-fuzzy systems
Autoren
Philipp Maximilian Sieberg MSc, Markus Schmid BSc, Sebastian Reicherts MSc, Prof. Dr.-Ing. Dr. h.c. Dieter Schramm, Universität Duisburg-Essen, Lehrstuhl für Mechatronik
Jahr
2019
Zusammenfassung
The present article discusses the possibility to reduce the computational effort of com-plex control algorithms by neuro-fuzzy systems. Thereby, great potentials can be released, especially in the automotive sector. A limiting factor for the design of control algorithms is the task of a real-time execution on cost-optimized control units. The influence of this limitations can be reduced by neuro-fuzzy systems. This is shown ex-emplary for the model-based predictive control of the roll motion presented by Sieberg et al. The controller based on the adaptive neuro-fuzzy inference system is validated regarding the control quality and the computational effort. Thus it is compared to the origin model-based predictive control algorithm. The implementation and validation are based on a co-simulation of MATLAB/SIMULINK and IPG CarMaker.
Mitglieder des Österreichischen Vereins für Kraftfahrzeugtechnik haben Zugriff auf alle Vorträge der Internationalen Wiener Motorensymposien.