Intelligent based terrain preview controller for a 3-axle vehicle

Economou, John T.; Purdy, David J.; Galvão Wall, David; Diskett, D.; Simner, D.

dc.contributor.author	Economou, John T.
dc.contributor.author	Purdy, David J.
dc.contributor.author	Galvão Wall, David
dc.contributor.author	Diskett, D.
dc.contributor.author	Simner, D.
dc.date.accessioned	2016-10-18T15:35:27Z
dc.date.available	2016-10-18T15:35:27Z
dc.date.issued	2016-09-16
dc.identifier.citation	Economou J, Purdy D, Galvao Wall D, Diskett D & Simner D (2016) Intelligent based terrain preview controller for a 3-axle vehicle. In: 13th International Symposium on Advanced Vehicle Control, AVEC'16, Munich, 13-16 September 2016.	en_UK
dc.identifier.uri	http://dspace.lib.cranfield.ac.uk/handle/1826/10777
dc.identifier.uri	https://www.crcpress.com/Advanced-Vehicle-Control-Proceedings-of-the-13th-International-Symposium/Edelmann-Plochl-Pfeffer/p/book/9781315265285
dc.description	Presented at 13th International Symposium on Advanced Vehicle Control, AVEC'16; Munich 13-16/09/2016	en_UK
dc.description.abstract	The paper presents a six-wheel half longitudinal model and the design of a dual level control architecture. The first (top) level is designed using a Sugeno fuzzy inference feedforward architecture with and without preview. The second level of controllers are locally managing each wheel for each axle. As the vehicle is moving forward the front wheels and suspension units will have less time to respond when compared to the middle and rear units, hence a preview sensor is used to compensate. The paper shows that the local active suspensions together with the Sugeno Fuzzy, (locally optimised using subtractive clustering), Feedforward control strategy is more effective and this architecture has resulted in reducing the sprung mass vertical acceleration and pitch accelerations.
dc.publisher	AVEC 16	en_UK
dc.title	Intelligent based terrain preview controller for a 3-axle vehicle	en_UK
dc.type	Conference paper	en_UK