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Intelligent automotive braking systemKees, Markus January 2002 (has links)
No description available.
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Heavy vehicle wheel slip controlKienhöfer, Frank Werner January 2011 (has links)
No description available.
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Fuzzy control for antilock braking and antislip regulation of wheels.De Koker, Pieter Marius 17 August 2012 (has links)
M.Ing. / Adaptive traction control can greatly enhance the mobility of vehicles on varying road surfaces. Traction control includes Antilock Braking Systems (ABS) and Antislip Regulation Systems (ASR). During braking, wheel slip is controlled with ABS, while wheel slip during acceleration is controlled by an ASR. Since the friction between a vehicle's tyre and the road surface is a function of wheel slip, there is an optimum wheel slip value at which the best road holding performance can be achieved. This optimum wheel slip value will however change with differing road surfaces and vehicle speeds. Optimising the wheel slip values has several advantages: both vehicle stopping and acceleration distances can be optimised, vehicle handling during in-turn braking and acceleration are optimised and tyre wear is reduced. Currently there are various ABS and ASR systems available, with the common goal of optimising wheel slip. These systems are however mechanically complex, while the operation of both these systems is usually triggered when some wheel slip value is exceeded. Differing road surfaces can greatly influence the effectiveness of these systems. The central theme of this research is the development of a fuzzy logic control algorithm for vehicle traction control. The control algorithm is tested with an experimental setup. The operating conditions experienced by an ABS are closely simulated in order to study the feasibility of fuzzy logic for traction control. The results obtained indicates the viability of fuzzy logic for wheel slip control. Confirmation of these results, obtained with the experimental ABS, have to be validated during vehicle testing. The main goal is to improve the performances of existing traction control systems and the feasibility of fuzzy controllers in automobile applications.
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Adaptive control of nonlinear systems using neural networks by Sanjay Kumar Mazumdar.Mazumdar, Sanjay Kumar January 1995 (has links)
Bibliography : leaves 238-262. / xxiii, 262 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1995
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