A fuzzy logic controller for an ABS braking system

Mauer, Georg

doi:10.1109/91.481947

Cited by 186 publications

(97 citation statements)

References 4 publications

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“…Examples are: model based adaptive control (Su, Chang, & Chen, 2006) and sliding modes control (Kayacan, Oniz, & Kaynak, 2009), fuzzy control (Mauer, 1995), neuro-fuzzy control (Wang et al, 2009), genetic neural control (Lee & Zak, 2001). Fuzzy logic in particular seems to be an interesting choice because of its good compromise between tuning simplicity -contrarily to neuro-fuzzy and genetic neural control -and robustness to disturbances and parameter variations -it is independent of complex vehicle and brake models.…”

Section: Slip-based Braking Systemmentioning

confidence: 99%

Vision-based active safety system for automatic stopping

Milanés

Llorca

Villagrá

et al. 2012

Expert Systems with Applications

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Intelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate -obtained via a vision-based system -and the wheel-locking probability -obtained via the vehicle's CAN bus -that generates a safe braking action. The system has been tested in a real car -a convertible Citroen C3 Pluriel -equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle's original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver.

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Section: Slip-based Braking Systemmentioning

confidence: 99%

Vision-based active safety system for automatic stopping

Milanés

Llorca

Villagrá

et al. 2012

Expert Systems with Applications

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“…controller model d time delay samples e (k) error between input and output f activation function G(z) system dynamics n1 number of neurons, 1st hidden layer P(z) predictor model p1, p2 pressures in actuator chamber q inputs number of a neural network r, r(k) demand reference u, u(k) control signal wj weight vector in activation function wji element i in wj xi neural network input element i y, y(k) system output, the actuator position y, y(k) output prediction INTRODUCTION PID control is the most widely used method in industrial application though a variety of advance control schemes have been developed and some have found places in practical application, such as adaptive control, model predictive control and fuzzy logic control [1][2][3]. Conventional PID control to time delay…”

Section: Normenclature C Output Of a Neuron D(z)mentioning

confidence: 99%

Position Control of a Pneumatic Servoactuator With Time Delay Using a Neural Network Predictor

Xue

Watton

2002

Proceedings of the JFPS International Symposium on Fluid Power

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ABASTRACTConsideration is given to data-based dynamic modelling and positioning of a pneumatic servo-actuator with time delay due to friction and air compressibility. A Smith predictor-type control scheme is considered to improve the control behaviour. System non-linearity is verified by the inability of linear modelling to provide satisfactory prediction. Data based dynamic modelling is undertaken using the group method of data handling(GMDH) neural network, a feedforward type neural network, and the techniques show potential for prediction of non-linear behaviour. Hence the time delay effect to the closed-loop control can be removed by incorporating the trained nonlinear model as a predictor in the feedback path. Conventional feedback control with/without the predictor are compared, and it is shown that the time delay system stability is significantly improved by using the non-linear predictor. The effect of time delay is most significant at small stroke changes of fast response demand and experiments were undertaken around the mid-stroke, which is less damped than any other position.

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“…A number of investigations have been proposed different control techniques for achieving improved braking performance of the HEVs [18] that employ electric vehicles such as fuzzy logic control [4][5][6], neural network control [1,7], feedback linearization control [8,17], iterative learning control [9], and sliding mode control [1][2][3]. All these control approaches intended to control slip ratio accurately thereby reducing the stopping distance by preventing wheel lockup with simultaneously providing directional control and stability.…”

Section: Introductionmentioning

confidence: 99%

Effects of sliding surface on the performances of adaptive sliding mode slip ratio controller for a HEV

Dash¹,

Subudhi²

2013

Archives of Control Sciences

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Effects of sliding surface on the performances of adaptive sliding mode slip ratio controller for a HEV BASANTA KUMAR DASH and BIDYADHAR SUBUDHI Slip ratio control of a ground vehicle is an important concern for the development of antilock braking system (ABS) to avoid skidding when there is a transition of road surfaces. In the past, the slip ratio models of such vehicles were derived to implement ABS. It is found that the dynamics of the hybrid electric vehicle (HEV) is nonlinear, time varying and uncertain as the tire-road dynamics is a nonlinear function of road adhesion coefficient and wheel slip. Sliding mode control (SMC) is a robust control paradigm which has been extensively used successfully in the development of ABS of a HEV. But the SMC performance is influenced by the choice of sliding surface. This is due to the discontinuous switching of control force arising in the vicinity of the sliding surface that produces chattering. This paper presents a detailed study on the effects of different sliding surfaces on the performances of sliding mode based adaptive slip ratio control applied to a HEV.

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A fuzzy logic controller for an ABS braking system

Cited by 186 publications

References 4 publications

Vision-based active safety system for automatic stopping

Vision-based active safety system for automatic stopping

Position Control of a Pneumatic Servoactuator With Time Delay Using a Neural Network Predictor

Effects of sliding surface on the performances of adaptive sliding mode slip ratio controller for a HEV

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