Antilock Braking System (ABS) Based Control Type Regulator Implemented by Neural Network in Various Road Conditions

Chen, Hsing‐Chung; Wisnujati, Andika; Widodo, Agung; Song, Yen-Ling; Lung, Chi-Wen

doi:10.1007/978-3-030-84913-9_21

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…When the energy vehicle is braked urgently, the wheel is subjected to the braking torque generated by the regenerative braking system and the braking torque generated by the friction brake at the same time [ 4 ]. How to distribute the relationship between friction braking and regenerative braking has become a key issue for regenerative braking systems [ 5 ]. If the braking energy regeneration system and the ABS system are reasonably coordinated and controlled, the economy of the vehicle can be improved, and driving safety can be enhanced [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Zhang

2022

Computational Intelligence and Neuroscience

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Aiming at the energy vehicle ABS kinetic energy recovery, this study optimizes the ABS system through the IPSO-ELM model, so that the energy vehicle can recover the energy generated by the ABS system to the greatest extent, so as to achieve the purpose of kinetic energy recovery and reduce energy consumption and vehicle cost. Based on the PSO-ELM model, a linear decreasing weighting method is introduced, and then, an IPSO-ELM model is proposed for the optimization analysis of ABS brake kinetic energy recovery. The results show that compared with the simple ELM model and PSO-ELM model, the simulation mean square error and relative error are significantly smaller, the generalization ability and prediction accuracy are higher, and the maximum relative error of the prediction result is 5.43% and the average relative error is 2.72%. The results confirm that the use of IPSO-ELM for ABS kinetic energy recovery optimization is extremely effective, and the study of ABS kinetic energy recovery for energy vehicles based on IPSO-ELM model optimization has strong application prospects and application potential.

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Section: Introductionmentioning

confidence: 99%

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Zhang

2022

Computational Intelligence and Neuroscience

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“…From the literature review, several studies have proposed a number of advanced control approaches for the ABS, such as Fuzzy Logic Controller (FLC) [26], neural network [27], adaptive control [28]. Also, the sliding mode control has been adopted for the design of an ABS used on an electric vehicle which insures a robust performance against time-dependent parameters variations, disturbances, and simplicity of the physical realization [24,29].…”

Section: Controllers Of Antilock Braking Systemmentioning

confidence: 99%

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

Khadr,

Houidi,

Romdhane

2024

Eng. Res. Express

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This paper investigates the design of an Antilock Braking System for a Two Wheeled Vehicle using a non-linear multi-body dynamic model developed by an approach widely used in robotics. The Antilock Braking System is controlled using Matlab/Simulink by applying the Bang-Bang, Proportional-Integral-Derivative and Fuzzy Logic controllers in order to set the longitudinal slip to a desired value. The developed model is used to study the braking performances of a Two Wheeled Vehicle in a straight-line maneuver under different road conditions: dry, wet and a transition between both. The simulation results indicate that the non-linear dynamic model can effectively simulate the behavior of the Two Wheeled Vehicle under emergency braking conditions and with the different types of controllers. The braking performance of the Two Wheeled Vehicle with Antilock Braking System was improved by decreasing the stopping distance, avoiding wheel lockup and therefore contributes to ensure the vehicle control and stability and to reduce the risk of falling. Also, it can be seen through comparative analysis that the Fuzzy logic is one of the most efficient controllers that improve the braking safety.

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Antilock Braking System (ABS) Based Control Type Regulator Implemented by Neural Network in Various Road Conditions

Cited by 2 publications

References 14 publications

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

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