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

Modelling and Development of Linear and Nonlinear Intelligent Controllers for Anti-lock Braking Systems (ABS)

 نظام  منع انغلاق المكابح (ABS)  يستخدم كجزء مهم في المركبات الحديثة لمنع الاطار من الغلق بعد تعشيق المكابح. الاداء العام لنظام سيطرة  منع انغلاق المكابح مستفيدا من كون النظام خطياً او غير خطي موضحاً في هذا البحث. من اجل تصميم نظام السيطرة، تم اشتقاق نموذج ديناميكي لاخطي لمانع الانزلاق استناداً على طبيعه نظامه الفيزيائي. النموذج الديناميكي متكون من عدة معادلات تحكم عمل النظام الميكانيكي.  نظامين سيطرة محتلفين تم استخدامهم للسيطرة على اداء  منع انغلاق المكابح، الاول تم الاستفادة من المسيطر الخطي نوع (PID) مع استخدام تقنية تحويل النظام من اللاخطي الى الخطي حول نقطة معينه للسيطرة على النظام اللاخطي. بينما تم استخدام المسيطر اللاخطي الثاني نوع (discrete time) للسيطرة على النظام الديناميكي اللاخطي بشكل مباشر. هذه الدراسة اعطت معلومات اضافية حول كيفية محاكاة هذين المسيطرين، و اعطت افضلية واضحة للمسيطر التقليدي (PID) على المسيطر نوع (discrete time) في السيطرة و التحكم بنظام منع انغلاق المكابح.Antilock braking systems (ABS) are utilized as a part of advanced autos to keep the vehicle’s wheels from deadlocking when the brakes are connected. The control performance of ABS utilizing linear and nonlinear controls is cleared up in this research. In order to design the control system of ABS a nonlinear dynamic model of the antilock braking systems is derived relying upon its physical system. The dynamic model contains set of equations valid for simulation and control of the mechanical framework. Two different controllers technique is proposed to control the behaviors of ABS. The first one utilized the PID controller with linearized technique around specific point to control the nonlinear system, while the second one used the nonlinear discrete time controller to control the nonlinear mathematical model directly. This investigation contributes to more additional information for the simulation of the two controllers, and demonstrates a clear and reasonable advantage of the classical PID controller on the nonlinear discrete time controller in control the antilock braking system

A Modified HOSM Controller Applied to an ABS Laboratory Setup with Adaptive Parameter

The antilock braking system (ABS) is an electromechanical device whose controller is challenging to design because of its nonlinear dynamics and parameter uncertainties. In this paper, an adaptive controller is considered under the assumption that the friction coefficient is unknown. A modified high-order sliding-mode controller is designed to enhance the controller performance. The controller ensures tracking of the desired reference and identifies the unknown parameter, despite parametric variations acting on the real system. The stability proof is done using the Lyapunov approach. Some numerical and experimental tests evaluate the controller on a mechatronic system that represents a quarter-car model

Dynamic Control Applied to a Laboratory Antilock Braking System

The control of an antilock braking system is a difficult problem due to the existence of nonlinear dynamics and uncertainties of its characteristics. To overcome these issues, in this work, a dynamic nonlinear controller is proposed, based on a nonlinear observer. To evaluate its performance, this controller has been implemented on an ABS Laboratory setup, representing a quarter car model. The nonlinear observer reconstructs some of the state variables of the setup, assumed not measurable, to establish a fair benchmark for an ABS system of a real automobile. The dynamic controller ensures exponential convergence of the state estimation, as well as robustness with respect to parameter variations

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

Modelling & development of antilock braking system

Antilock braking systems are used in modern cars to prevent the wheels from locking after brakes are applied. The dynamics of the controller needed for antilock braking system depends on various factors. The vehicle model often is in nonlinear form. Controller needs to provide a controlled torque necessary to maintain optimum value of the wheel slip ratio. The slip ratio is represented in terms of vehicle speed and wheel rotation. In present work first of all system dynamic equations are explained and a slip ratio is expressed in terms of system variables namely vehicle linear velocity and angular velocity of the wheel. By applying a bias braking force system, response is obtained using Simulink models. Using the linear control strategies like P - type, PD - type, PI - type, PID - type the effectiveness of maintaining desired slip ratio is tested. It is always observed that a steady state error of 10% occurring in all the control system models

Development of an Electronic Stability Control for Improved Vehicle Handling using Co-Simulation

The research project focuses on integrating the algorithms of recent automotive Electronic Stability Control (ESC) technologies into a commercial multi-body dynamics (MBD) software for full vehicle simulations. Among various control strategies for ESC, the sliding mode control (SMC) method is proposed to develop these algorithms, as it is proven to be excellent at overcoming the effect of uncertainties and disturbances. The ESC model integrates active front steering (AFS) system and direct yaw moment control (DYC) system, using differential braking system, therefore the type of the ESC model is called as integrated vehicle dynamic control (IVDC) system. The IVDC virtual model will be designed using a specialized control system software, called Simulink. The controller model will be used to perform full vehicle simulations, such as sine with dwell (SwD) and double lane change (DLC) tests on Simulink to observe its functionality in stabilizing vehicles. The virtual nonlinear full vehicle model in CarSim will be equipped with the IVDC virtual model to ensure that the proposed IVDC virtual model passes the regulations that describes the ESC homologation process for North America and European countries, each defined by National Highway Traffic Safety Administration (NHTSA) and United Nations (UN). The proposed research project will enable automotive engineers and researchers to perform full vehicle virtual simulations with ESC capabilities

Vision-based active safety system for automatic stopping

ntelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver

State and parameter estimator design for control of vehicle suspension system

Modern vehicle stability and navigational systems are mostly designed using inaccurate bicycle models to approximate the full-car models. This results in incomplete models with various unknown parameters and states being neglected in the controller and navigation system design processes. Earlier estimation algorithms using the bicycle models are simpler but have many undefined parameters and states that are crucial for proper stability control. For existing vehicle navigation systems, direct line of sight for satellite access is required but is limited in modern cities with many high-rise buildings and therefore, an inertial navigation system utilizing accurate estimation of these parameters is needed. The aim of this research is to estimate the parameters and states of the vehicle more accurately using a multivariable and complex full-car model. This will enhance the stability of the vehicle and can provide a more consistent navigation. The proposed method uses the kinematics estimation model formulated using special orthogonal SO3 group to design estimators for vehicles velocity, attitude and suspension states. These estimators are used to modify the existing antilock braking system (ABS) scheme by incorporating the dynamic velocity estimation to reduce the stopping distance. Meanwhile the semi-active suspension system includes suspension velocity and displacement states to reduce the suspension displacements and velocities. They are also used in the direct yaw control (DYC) scheme to include mass and attitude changes to reduce the lateral velocity and slips. Meanwhile in the navigation system, the 3-dimensional attitude effects can improve the position accuracy. With these approaches, the stopping distance in the ABS has been reduced by one meter and the vehicle states required for inertial navigation are more accurately estimated. The results for high speed lane change test indicate that the vehicle is 34% more stable and 16% better ride comfort on rough terrains due to the proposed DYC and the active suspension system control. The methods proposed can be utilized in future autonomous car design. This research is therefore an important contribution in shaping the future of vehicle driving, comfort and stability