2019 | OriginalPaper | Buchkapitel
Computation time optimization of a model-based predictive roll stabilization by neuro-fuzzy systems
verfasst von : Philipp Maximilian Sieberg, Markus Schmid, Sebastian Reicherts, Dieter Schramm
Erschienen in: 19. Internationales Stuttgarter Symposium
Verlag: Springer Fachmedien Wiesbaden
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The present article discusses the possibility to reduce the computational effort of complex 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 [1]. The influence of this limitations can be reduced by neuro-fuzzy systems. This is shown exemplary for the model-based predictive control of the roll motion presented by Sieberg et al. [2]. 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.