An Intelligent Tire Based Tire-Road Friction Estimation Technique and Adaptive Wheel Slip Controller for Antilock Brake System

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Kanwar B. Singh ◽  
Mustafa Ali Arat ◽  
Saied Taheri
Keyword(s):  
Parameter Estimation ◽  
Friction Coefficient ◽  
Control Systems ◽  
Real Time ◽  
Time Estimate ◽  
Slip Conditions ◽  
The Road ◽  
Road Friction ◽  
Friction Estimation ◽  
Road Friction Coefficient

The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, due to changes to the road conditions, the driver's ability to maintain a stable vehicle may be at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from an intelligent tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a brush model-based nonlinear least squares (NLLS) parameter estimation approach is proposed. Hence an integrated approach using the intelligent tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of the ABS control systems. In order to achieve the aforementioned objectives, the design and implementation of a fuzzy/sliding mode/proportional integral (fuzzy-SMC-PI (FSP)) control methodology is proposed. The results show significant improvements in the stopping distance of a vehicle equipped with an intelligent tire based FSP controller as compared to a vehicle equipped with a standard ABS.

Development of a Smart Tire System and its Use in Improving the Performance of a Collision Mitigation Braking System

2012 ◽  
Author(s):  
Kanwar Bharat Singh ◽  
Mustafa Ali Arat ◽  
Saied Taheri
Keyword(s):  
Parameter Estimation ◽  
Friction Coefficient ◽  
Control Systems ◽  
Real Time ◽  
Vehicle Control ◽  
Slip Conditions ◽  
Model Based ◽  
Road Friction ◽  
Collision Mitigation ◽  
Road Friction Coefficient

At present, the commercially available tire monitoring systems are not equipped to sense and transmit high speed dynamic variables used for real-time active safety control systems. Hence, today’s vehicle control systems are limited by the lack of knowledge of critical tire-road states (i.e. the kinematic conditions of the tire to its dynamic properties). From aforementioned discussion, it is clear that some method of estimating tire-road contact parameters would be greatly desirable. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from a smart tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a tire-road friction model-based parameter estimation approach is proposed. Hence an integrated approach using the smart tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of collision mitigation braking systems.

GPS-Based Real-Time Identification of Tire-Road Friction Coefficient

2002 ◽  
Author(s):  
J. O. Hahn ◽  
R. Rajamani ◽  
L. Alexander
Keyword(s):  
Friction Coefficient ◽  
Control Systems ◽  
Real Time ◽  
Identification System ◽  
Force Model ◽  
Identification Algorithm ◽  
Cruise Control ◽  
Differential Gps ◽  
Road Friction ◽  
Road Friction Coefficient

Vehicle control systems such as collision avoidance, adaptive cruise control and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made “road-adaptive”. The estimation of tire-road friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-road friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential GPS system and a gyroscope is used to identify the tire-road friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable friction estimates. Simulation studies indicate that a parameter convergence rate of one second can be obtained. Experiments conducted on both dry and slippery road indicate that the algorithm can work very effectively in identifying a slippery road. Two other new approaches to real-time tire road friction identification system are also discussed in the paper.

Comprehensive prediction method of road friction for vehicle dynamics control

2009 ◽  
Vol 223 (8) ◽  
pp. 987-1002 ◽  
Author(s):  
L Li ◽  
J Song ◽  
H-Z Li ◽  
D-S Shan ◽  
L Kong ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Control Systems ◽  
Vehicle Dynamics ◽  
Control Mode ◽  
Fusion Method ◽  
The Road ◽  
Comprehensive Method ◽  
Road Friction ◽  
Vehicle Dynamics Control ◽  
Road Friction Coefficient

The contact friction characteristic between a tyre and the road is a key factor that dominates the dynamics performance of a vehicle under critical conditions. Vehicle dynamics control systems, such as anti-lock braking systems, traction control systems, and electronic stability control systems (e.g. Elektronisches Stabilitäts Programm (ESP)), need an accurate road friction coefficient to adjust the control mode. No time delay in the estimation of road friction should be allowed, thereby avoiding the disappearance of the optimal control point. A comprehensive method to predict the road friction is suggested on the basis of the sensor fusion method, which is suitable for variations in the vehicle dynamics characteristics and the control modes. The multi-sensor signal fusion method is used to predict the road friction coefficient for a steering manoeuvre without braking; if active braking is involved, simplified models of the braking pressure and tyre force are adopted to predict the road friction coefficient and, when high-intensity braking is being considered, the neural network based on the optimal distribution method of the decay power is applied to predict the road friction coefficient. The method is validated through a ground test under complicated manoeuvre conditions. It was verified that the comprehensive method predicts the road friction coefficient promptly and accurately.

Estimation of the Maximum Tire-Road Friction Coefficient

2003 ◽  
Vol 125 (4) ◽  
pp. 607-617 ◽  
Author(s):  
Steffen Mu¨ller ◽  
Michael Uchanski ◽  
Karl Hedrick
Keyword(s):  
Friction Coefficient ◽  
Estimation Algorithm ◽  
Road Surface ◽  
Measurement Noise ◽  
Misclassification Rate ◽  
The Road ◽  
Subtle Effect ◽  
Road Friction ◽  
Friction Estimation ◽  
Road Friction Coefficient

We develop and test a “slip-based” method to estimate the maximum available tire-road friction during braking. The method is based on the hypothesis that the low-slip, low-μ parts of the slip curve used during normal driving can indicate the maximum tire-road friction coefficient, μmax. We find support for this hypothesis in the literature and through experiments. The friction estimation algorithm uses data from short braking maneuvers with peak accelerations of 3.9 m/s2 to classify the road surface as either dry μmax≈1 or lubricated μmax≈0.6. Significant measurement noise makes it difficult to detect the subtle effect being measured, leading to a misclassification rate of 20%.

A review on identification methods of road friction coefficient

2021 ◽  
Vol 15 ◽  
Author(s):  
Gengxin Qi ◽  
Xiaobin Fan ◽  
Hao Li
Keyword(s):  
Friction Coefficient ◽  
Autonomous Driving ◽  
Research Direction ◽  
Identification Methods ◽  
Research Status ◽  
The Road ◽  
Road Friction ◽  
Coefficient Measurement ◽  
Estimation System ◽  
Road Friction Coefficient

Background: The development of the tire/road friction coefficient measurement and estimation system has far-reaching significance for the active electronic control safety system of automobiles and is one of the core technologies for autonomous driving in the future. Objective: Estimating the road friction coefficient accurately and in real-time has become the leading research direction. Researchers have used different tools and proposed different algorithms and patents. These methods are widely used to estimate the road friction coefficient or other related parameters. This paper gives a comprehensive description of the research status in the field of road friction coefficient estimation. Method: According to the current research status of Chinese and foreign scholars in the field of road friction coefficient recognition, the recognition methods are mainly divided into two categories: Cause-based and Effect-based. Results: This literature review will discuss the existing two types of identification methods (Cause-based and Effect-based), and the applicable characteristics of each algorithm are analyzed. Conclusion: The two recognition methods are analyzed synthetically, and the development direction of road friction coefficient recognition technology is discussed.

Hierarchical Nonlinear Moving Horizon Estimation of Vehicle Lateral Speed and Road Friction Coefficient

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Chi Jin ◽  
Anson Maitland ◽  
John McPhee
Keyword(s):  
Friction Coefficient ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Global Optimum ◽  
Moving Horizon Estimation ◽  
The Road ◽  
Nominal Model ◽  
Road Friction ◽  
High Level ◽  
Road Friction Coefficient

Abstract In this paper, we address nonlinear moving horizon estimation (NMHE) of vehicle lateral speed, as well as the road friction coefficient, using measured signals from sensors common to modern series-production automobiles. Due to nonlinear vehicle dynamics, a standard nonlinear moving horizon formulation leads to non-convex optimization problems, and numerical optimization algorithms can be trapped in undesirable local minima, leading to incorrect solutions. To address the challenge of non-convex cost functions, we propose an estimator with a two-level hierarchy. At the high level, a grid search combined with numerical optimization aims to find reference estimates that are sufficiently close to the global optimum. The reference estimates are refined at the low level leading to high-precision solutions. Our algorithm ensures that the estimates converge to the true values for the nominal model without the need for accurate initialization. Our design is tested in simulation with both the nominal model as well as a high-fidelity model of Autonomoose, the self-driving car of the University of Waterloo.

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