Application of Fuzzy control algorithms for electric vehicle antilock braking/traction control systems

Abstract—The application of fuzzy-based control strategies has recently gained enormous recognition as an approach for the rapid development of effective controllers for nonlinear time-variant systems. This paper describes the preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs). As the dynamics of the braking systems are highly nonlinear and time variant, fuzzy control offers potential as an important tool for development of robust traction control. Simulation studies are employed to derive an initial rule base that is then tested on an experimental test facility representing the dynamics of a braking system. The test facility is composed of an induction machine load operating in the generating region. It is shown that the torque-slip characteristics of an induction motor provides a convenient platform for simulating a variety of tire/road - driving conditions, negating the initial requirement for skid-pan trials when developing algorithms. The fuzzy membership functions were subsequently refined by analysis of the data acquired from the test facility while simulating operation at a high coefficient of friction. The robustness of the fuzzy-logic slip regulator is further tested by applying the resulting controller over a wide range of operating conditions. The results indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions where classical ABS/traction control schemes are constrained to operate very conservatively

Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC

Time-Varying Sliding Mode Control for ABS Control of an Electric Car

Controller design for the Anti-Lock Braking System (ABS) of a wheeled vehicle is a challenging task because of the complex and nonlinear nature of the tyre-road interaction. An efficient ABS controller should be capable of maintaining the wheel slip at an optimal value, which is suitable for the particular road conditions experienced at a given instant in time, preventing the wheel from locking while braking. Many controller designs in the literature track either an optimal slip which is assumed constant or are not supported by experimental validation or simulation testing with higher order models. This paper first presents an ABS system based on a conventional Sliding Mode Control (SMC). The performance of this controller is tested on an experimental vehicle. The results are compared with simulation results obtained with both a quarter car model and a full-car model built in the Matlab/Simulink environment. The performance of this controller is improved by effective state estimation using a Sliding Mode Differentiator (SMD) where the results are benchmarked with an implementation using an Extended Kalman Filter (EKF). The paper then presents a controller based on Time-Varying Sliding Mode Control (TV-SMC) which tracks an optimal slip trajectory

Second Order Sliding Mode Controller for Longitudinal Wheel Slip Control

This paper investigates the longitudinal wheel slip tracking control approach for ground vehicle. A mathematical model of a quarter vehicle undergoing a straight-line braking maneuver is used as the control model. Second order sliding mode (SOSM) control approach using super-twisting technique is proposed to manipulate the braking torque to control the wheel slip. The effectiveness of the SOSM is compared to the conventional sliding mode in the simulations of emergency straight line braking in Simulink. With the SOSM, the chattering phenomenon is eliminated, giving a smooth tracking trajectory and lower slip error and control effort

Integral high order sliding mode control of a brake system

The aim of this paper is to present the design of a robust sliding mode control scheme for a vehicle system which consists of active brake systems. The proposed control strategy is based on the combination of high order sliding mode control methods and integral sliding mode control, taking advantage of the block control principle. The brake controller induces the antilock brake system feature by means of tracking the slip rate of the car, improving the stability in the braking process and preventing the vehicle from skidding.Cinvesta

On-line learning applied to spiking neural network for antilock braking systems

Computationally replicating the behaviour of the cerebral cortex to perform the control tasks of daily life in a human being is a challenge today. First, … Finally, a suitable learning model that allows adapting neural network response to changing conditions in the environment is also required. Spiking Neural Networks (SNN) are currently the closest approximation to biological neural networks. SNNs make use of temporal spike trains to deal with inputs and outputs, thus allowing a faster and more complex computation. In this paper, a controller based on an SNN is proposed to perform the control of an anti-lock braking system (ABS) in vehicles. To this end, two neural networks are used to regulate the braking force. The first one is devoted to estimating the optimal slip while the second one is in charge of setting the optimal braking pressure. The latter resembles biological reflex arcs to ensure stability during operation. This neural structure is used to control the fast regulation cycles that occur during ABS operation. Furthermore, an algorithm has been developed to train the network while driving. On-line learning is proposed to update the response of the controller. Hence, to cope with real conditions, a control algorithm based on neural networks that learn by making use of neural plasticity, similar to what occurs in biological systems, has been implemented. Neural connections are modulated using Spike-Timing-Dependent Plasticity (STDP) by means of a supervised learning structure using the slip error as input. Road-type detection has been included in the same neural structure. To validate and to evaluate the performance of the proposed algorithm, simulations as well as experiments in a real vehicle were carried out. The algorithm proved to be able to adapt to changes in adhesion conditions rapidly. This way, the capability of spiking neural networks to perform the full control logic of the ABS has been verified.Funding for open access charge: Universidad de Málaga / CBUA This work was partly supported by the Ministry of Science and Innovation under grant PID2019-105572RB-I00, partly by the Regional Government of Andalusia under grant UMA18-FEDERJA-109, and partly by the University of Malaga as well as the KTH Royal Institute of Technology and its initiative, TRENoP

Cooperative Control of Regenerative Braking and Antilock Braking for a Hybrid Electric Vehicle

A new cooperative braking control strategy (CBCS) is proposed for a parallel hybrid electric vehicle (HEV) with both a regenerative braking system and an antilock braking system (ABS) to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC) for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC), and the motor speed, a fuzzy logic control strategy (FLC) is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency