Fuzzy learning control for antiskid braking systems

Layne, J.R.; Passino, K.M.; Yurkovich, Stephen

doi:10.1109/87.238405

Cited by 166 publications

(52 citation statements)

References 8 publications

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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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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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“…Behmenburg (1993) apresentou o ajuste de um controlador nebuloso por modelo de referência usando o modelo inverso do processo e uma abordagem baseada em gradiente para adaptar os parâmetros do controlador. Layne et al (1993) introduziram a idéia de aprendizado de controle nebuloso usando modelo de referência para aplicações automotivas. Liaw e Lin (1995) desenvolveram um controlador com dois graus de liberdade com modelo de referência seguindo um procedimento de adaptação nebulosa.…”

Section: Introductionunclassified

Controle nebuloso adaptativo por modelo de referência: projeto e aplicação em sistemas não lineares

Callai

Coelho

2007

Sba Controle & Automação

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The main contribution of this paper is the investigation of different model reference adaptation schemes for improving the performance of fuzzy logic control. Adaptive fuzzy control strategies are based on: i) fuzzy control with fuzzy internal model; ii) fuzzy control with fuzzy compensation; and iii) proportional and integral control with fuzzy adaptation. Numerical and experimental results are shown in order to assess the performance of each adaptive fuzzy control algorithm in nonlinear processes.

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“…Therefore, intelligent controllers should be developed to deal with all these uncertainties. Many control strategies such as Sliding mode control (Harifi et al, 2005;Unsal, & Kachroo, 1999;Choi et al, 2002;Oniz, 2007;Oniz, et al, 2009), intelligent techniques using Fuzzy Logic (Mauer, 1995;Radac et al, 2008), Artificial Neural Networks (Layne et al, 1993;Lin & Hsu, 2003), and Neuro-fuzzy control (Topalov, et al, 2011) are reported earlier in literature. Genetic Algorithm is used in finding optimum values of fuzzy component (Yonggon & Stanislaw, 2002).…”

Section: Introductionmentioning

confidence: 99%

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Boopathi¹,

Abudhahir²

2015

J Engin Res

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Fuzzy learning control for antiskid braking systems

Cited by 166 publications

References 8 publications

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

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

Controle nebuloso adaptativo por modelo de referência: projeto e aplicação em sistemas não lineares

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

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