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

Identification of Intelligent Controls in Developing Anti-Lock Braking System

This paper presents about the development of an Antilock Braking System (ABS) using quarter vehicle model and a control structure is developed to represents an ABS and conventional braking model. Different type of controllers is proposed to develop the ABS model. Antilock braking system (ABS) is an important part in vehicle system to produce additional safety for driver. This system is known as one of the automobile’s active safety. In general, Antilock braking systems have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. In this paper, to deal with the strong nonlinearity in the design of ABS controller, an intelligent controller has been identified. The controllers such as PID and Fuzzy Logic are proposed to control the stopping distance and longitudinal slip of the wheel. Comparison results between these two controllers generated using Matlab SIMULINK

Modelling of automatic car braking system using fuzzy logic controller

The increasing rate of road accident is alarming and any vehicle without an effective brake system is prone to accident with apparently disastrous effect following. This is due to human errors in driving which involves reaction time delays and distraction. Automatic braking system will be developed to keep the vehicle steerable and stable and also prevent wheel lock and collision with an obstacle. The objectives of this study are to: design an obstacle detection model using ultrasonic sensors, model an antilock braking system, develop fuzzy logic rules for both detection and antilock braking system, and simulate the developed model using Simulink in MATLAB software to achieve high braking torque, optimal slip ratio and shorter stopping distance and time. The results show 22% improvement in braking torque thereby giving a shorter stopping time and distance when compared to the normal PID control.Keywords: Slip ratio, Model, Ultrasonic Sensor, Antilock Braking System, Fuzzy logic, wheel loc

Identification of Intelligent Controls in Developing Antilock Braking System

This paper presents about the development of an Antilock Braking System (ABS) using quarter vehicle model and a control structure is developed to represents an ABS and conventional braking model. Different type of controllers is proposed to develop the ABS model. Antilock braking system (ABS) is an important part in vehicle system to produce additional safety for driver. This system is known as one of the automobile’s active safety. In general, Antilock braking systems have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. In this paper, to deal with the strong nonlinearity in the design of ABS controller, an intelligent controller has been identified. The controllers such as PID and Fuzzy Logic are proposed to control the stopping distance and longitudinal slip of the wheel. Comparison results between these two controllers generated using Matlab SIMULINK

IDENTIFICATION OF INTELLIGENT CONTROLS IN DEVELOPING ANTI-LOCK BRAKING SYSTEM

This paper presents about the development of an Antilock Braking System (ABS) using quarter vehicle model and a control structure is developed to represents an ABS and conventional braking model. Different type of controllers is proposed to develop the ABS model. Antilock braking system (ABS) is an important part in vehicle system to produce additional safety for driver. This system is known as one of the automobile’s active safety. In general, Antilock braking systems have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. In this paper, to deal with the strong nonlinearity in the design of ABS controller, an intelligent controller has been identified. The controllers such as PID and Fuzzy Logic are proposed to control the stopping distance and longitudinal slip of the wheel. Comparison results between these two controllers generated using Matlab SIMULINK

Arquitecturas de hardware para um veículo eléctrico

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Systematization of integrated motion control of ground vehicles

This paper gives an extended analysis of automotive control systems as components of the integrated motion control (IMC). The cooperation of various chassis and powertrain systems is discussed from a viewpoint of improvement of vehicle performance in relation to longitudinal, lateral, and vertical motion dynamics. The classification of IMC systems is proposed. Particular attention is placed on the architecture and methods of subsystems integration

Physical-Fingerprinting of Electronic Control Unit (ECU) Based on Machine Learning Algorithm for In-Vehicle Network Communication Protocol “CAN-BUS”

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.Master of Science in EngineeringComputer Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140731/1/Thesis manuscript_v3.pdfDescription of Thesis manuscript_v3.pdf : Thesi