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Braking Systems
Published in G. K. Awari, V. S. Kumbhar, R. B. Tirpude, Automotive Systems, 2021
G. K. Awari, V. S. Kumbhar, R. B. Tirpude
The function of the antilock braking system (ABS) is to avoid the locking of wheels, which will reduce the chances of the vehicle skidding or slipping. ABS senses the speed of retardation of the individual wheel and automatically delivers the pipeline pressure to increase or decrease the tendency of skidding, and the vehicle stops within the minimum possible time.
A Machine Learning Approach for Quantifying the Design Error Propagation in Safety Critical Software System
Published in IETE Journal of Research, 2022
An anti-lock braking system (ABS) is a real-time automotive safety system to avoid the wheels from locking when brake is applied by reacting to external interactions in a predetermined time. It is one of the important components of a modern automotive braking system. As a safety device, its main functions are to improve the direction stability of a braking vehicle, to ensure that the driver can still control the steering wheel in the braking process, thereby avoiding obstacles and to shorten the stopping distance.
Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road
Published in Automatika, 2019
Antilock braking system (ABS) ensures safe stopping by regulating the brake torques to provide maximum wheel traction force. This is conducted by estimating the optimal slip ratio, and enforcing it by a control technique. Thus, 3 algorithms are involved in an ABS: speed estimation, tire-road friction estimation, and control method.
A second-order slip model for constraint backstepping control of antilock braking system based on Burckhardt’s model
Published in International Journal of Modelling and Simulation, 2020
Youguo He, Chuandao Lu, Jie Shen, Chaochun Yuan
An anti-lock braking system (ABS) is one of major safety devices of vehicles, and it has a function to avoid vehicle wheel self-locking, shorten braking distance, and ensure the lateral stability according to the regulated wheel slip-ratio. The basic objective of ABS is to regulate wheel slip at its optimum value while maximising longitudinal tire-runway friction to generate large lateral force. Hence, many theoretical studies have been conducted on slip ratio control algorithms, such as nonlinear control [1], Fuzzy control [2], optimisation control [3], extremum seeking control [4], Adaptive Sliding mode controller [5,6], combined control [7], robust predictive control [8], fuzzy neural network controls [9], reinforcement Q-learning [10]. Mahanty and Subramanian presented a model-based slip control scheme to prevent the locking of wheels by regulating the brake chamber pressure for heavy-duty commercial vehicles [1]. In [2], by aiming at the problem of nonlinear ABS, a class of Takagi-Sugeno-Kang fuzzy models was proposed, and a synergy of fuzzy logic and nature-inspired optimisation was combined. In order to achieve a shorter stopping distance and maintain the vehicle in the straight line during a hard braking on a split-μ road, an optimal nonlinear algorithm based on the prediction of vehicle responses was presented [3]. In [4], an ABS control algorithm based on the extremum seeking searched the optimum slip ratio between the tire patch and the road without having to estimate road friction conditions. A sliding mode controller and a fractional calculus method were proposed for an ABS to adjust the wheel slip to the preferred value [5]. In [6], to improve the brake performance, especially when road condition changed, an adaptive fuzzy fractional-order sliding mode controller for ABS was designed by combining the fractional-order sliding mode controller and fuzzy logic controller. Lin et al. [7] proposed a novel method of realising a nonmechanical ABS controller for electric scooters. In this method, a boundary layer speed control was proposed to guarantee the optimal slip ratio between tires and road surface. Mirzaeinejad [8] developed an optimal control law for ABS based on a nonlinear predictive method and adopted a radial basis function neural network to overcome unknown certainties. In [9], to relax the requirement of detailed system dynamics, an intelligent exponential sliding-mode control system for an ABS was designed. Radac et al. [10] proposed the design and implementation of a model-free tire slip control for a fast and highly nonlinear ABS. In this method, a reinforcement Q-learning optimal control approach was inserted in a neural fitted scheme by using two neural networks to approximate the value function and the controller. These control algorithms have improved the response time and received the better performance of optimum slip ratio tracking.