Road Friction Estimation under Complicated Maneuver Conditions for Active Yaw Control

Road friction information is very important for vehicle active braking control systems such as ABS, ASR, or ESP. It is not easy to estimate the tire/road friction forces and coefficient accurately because of the nonlinear system, parameters uncertainties, and signal noises. In this paper, a robust and effective tire/road friction estimation algorithm for ABS is proposed, and its performance is further discussed by simulation and experiment. The tire forces were observed by the discrete Kalman filter, and the road friction coefficient was estimated by the recursive least square method consequently. Then, the proposed algorithm was analysed and verified by simulation and road test. A sliding mode based ABS with smooth wheel slip ratio control and a threshold based ABS by pulse pressure control with significant fluctuations were used for the simulation. Finally, road tests were carried out in both winter and summer by the car equipped with the same threshold based ABS, and the algorithm was evaluated on different road surfaces. The results show that the proposed algorithm can identify the variation of road conditions with considerable accuracy and response speed.

<div class="section abstract"><div class="htmlview paragraph">Accurate road friction coefficient is crucial for the proper functioning of active chassis control systems. However, road friction coefficient is difficult to be measured directly. Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. The proposed estimation algorithm is validated by both simulations and tests on a scaled model vehicle.</div></div>

Measurement of road friction coefficient using strain on tire sidewall

Vehicle states and the road friction coefficient in active safety control systems have become increasingly prominent. However, a low‐cost, high‐precision system in real‐time has yet to be achieved. The use of complex models has led to poor real‐time estimation, while variations in the road friction coefficient have often been neglected. This paper adopts information fusion technology by using DEKF theory for rapid simulation and estimation of these parameters. Using a vehicle dynamic model based on three degrees of freedom (3‐DOF) and the Highway Safety Research Institute tire model, DEKF recursive estimation models are established and verified. In the DEKF, two recursive state and parameter estimation models exist in parallel. The models are dependent upon each other and have real‐time interaction correction in order to forecast information, which quickly yields true value estimation in simulation. Experimental brake test results show that the DEKF estimator not only accurately estimates the vehicle state parameters, but also estimates the road friction coefficient in real‐time. This can reduce the cost of the vehicle sensor, and can estimate the status parameter, which is difficult to measure. The validity and feasibility of this algorithm have been verified by an HIL driving simulator, offering the possibility of future application in real cars.

Real-time knowledge of maximum road friction coefficient and road slope is crucial for active vehicle control systems. Most recent researches mainly assume that the road slope is constant and the influence on the identification of the maximum road friction coefficient is not considered. In this paper, the IMM Kalman filter method for the identification of road longitudinal slope is developed. Considering the axle load transfer caused by the road slope, the current road type is recognised by comparing the samples of the estimated instantaneous road friction coefficient with each standard road, then the maximum road friction coefficient can be easily determined by an online lookup table. Finally, experimental tests on the braking system hardware-in-the-loop test rig driven by the driving simulator are conducted. The results demonstrate that the identification approach can identify the current road slope and maximum road friction coefficient with good adaptability to various vehicle conditions.

Abstract: There are patents that consider some interesting characteristics as are [1] and [2] related with the pneumatic and in [3] the electrical elements for the rapid braking, in this paper, an uniformly stable observer for tire/road friction model estimation during braking process is proposed. In this observer the angular speed of the tire is taken as the output and it is used to estimate the internal friction state, the linear speed of the tire and the relative velocity of the vehicle [4]. It is proven that the state error of the observer applied to the nonlinear model is uniformly stable. The observer proposed is based on the solution of a Linear Matrix Inequality (LMI) equation [5]. A simulation shows the effectiveness of the suggested observer. Keywords: Braking process, observer, longitudinal dynamics. INTRODUCTION The tire/road friction phenomenon plays an important role in vehicle safety and handling. Researchers are often trying to improve the safety, driving comfort and traffic circulation. They also are trying to reduce the environmental pollution associated with vehicles. In this days, many con-trollers are available to improve safety and vehicle handling including anti-lock brake systems (ABS), traction control systems (TSC), adaptive intelligent cruise control, active yaw control systems and active suspension systems. How-ever these control systems only perform to their potential if they have access to the accurate information about the vehicle behavior in real time. Some authors have proposed the equations to model the dynamic behavior of the braking in a car. Two equations are related with the simplified motions of a wheel in a car are defined in [6-8], and other two equation are the Lumped LuGre friction model defined in [7-11]. In [12], they provide an excellent material for both service brake an parking brake. In [13], a device for selectively controlling and varying a frictional force level at an interface between two bodies was proposed. In [1], a pneumatic tire containing a combination plasticizer was proposed. In [2], a pneumatic tire with component containing asphaltene was proposed. In [4], improved methods and systems for controlling hydraulically or electrically actuated anti-lock brake systems (ABS) was proposed. In [3], the electrical engineering to processes for rapid braking was proposed.

For a modern vehicle, if a sensor in the vehicle Anti-lock Braking System (ABS) or the controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate and no methods can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation which considers real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of the attack) by minimizing errors between predicted vehicle states and measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from measured vehicle states to generate correct vehicle states for the vehicle ABS. We conducted first real sensor attack experiments to show how a magnet affects sensor readings. Our experimental results demonstrate that our attack detection method can detect sensor attack and CAN bus attack more accurately compared with existing methods, and also our attack mitigation strategy almost eliminates attack’s effects on the vehicle ABS.

Robust active yaw control for offshore wind farms using stochastic predictive control based on online adaptive scenario generation

Independently developing anti-lock braking system(ABS) is the basis of mastering the active dynamic control technology of vehicle chassis.According to the experimental data and the logic threshold control method widely used in the industrial ABS,the impact on ABS control of every logic threshold is analyzed and the basic rules of calibrating the logic thresholds is established.By borrowing the idea from the self-tuning regulator in adaptive control,a logic threshold self-adjusting algorithm is presented.The algorithm identifies the optimal brake slip ratio of the road and calculates the reference speed on-line.The main logic thresholds are adjusted automatically according to the error between the wheel slip ratio and the optimal slip ratio in order to improve the control performance.The control parameters are calibrated and the control performance of the self-adjusting algorithm is verified by road tests under various conditions.The test results indicate that the presented logic threshold self-adjusting algorithm overcomes the influence of different factors,such as road adhesion coefficient,speed and load,on the ABS,thereby the optimal ABS control under every condition is realized.The developed ABS has good control effect and meets the development requirement.It provides a foundation for ABS industrialization.

Estimation of Nonlinear Tire Forces and Road Friction for Modeling and Control

The aim of this paper is to improve vehicle crash worthiness using vehicle dynamics control systems (VDCS) integrated with an extendable front-end structure (extendable bumper). The work carried out in this paper includes developing and analyzing a new vehicle dynamics/crash mathematical model in case of vehicle-to-vehicle full frontal impact. This model integrates a vehicle dynamics model with the vehicle’s front-end structure to define the vehicle body crash kinematic parameters. In this model, the anti-lock braking system (ABS) and the active suspension control system (ASC) are co-simulated, and its associated equations of motion are developed and solved using Incremental Harmonic Balance Method (IHBM). An Under Pitch Control (UPC) technique is used to minimize the deformation zone, pitch angle and its acceleration. The simulations show considerable improvements using UPC along side ABS with and without the extendable bumper (EB), which produces additional significant improvements for both vehicle boy acceleration and intrusion.

This paper proposes a novel hybrid framework for estimating longitudinal tire forces by combining a model-based observer and a Transformer neural network architecture. A key advantage of the proposed approach is its independence from wheel torque measurements, which are commonly used in vehicle force estimation methods. The proposed framework first employs a model-based observer using the Pacejka tire model to generate preliminary estimates of longitudinal tire forces. These estimates are then refined by a neural network featuring an encoder-only Transformer architecture augmented with dilated convolutions, which effectively captures long-term temporal dependencies in sensor data while compensating for model inaccuracies. Additionally, our hybrid approach is able to estimate the longitudinal velocity and road friction coefficient, which are crucial for observer's preliminary force estimate in extreme road and driving conditions. Experimental validation on an electric Equinox vehicle under diverse driving scenarios-including highslip maneuvers on dry, wet, and icy roads-demonstrates the framework's superior performance compared to model-based and neural network-only approaches. The proposed method achieves robust estimation accuracy even under sensor faults (e.g., biased acceleration signals) and harsh driving conditions, indicating its potential for enhancing vehicle safety systems such as traction and stability control.

The prevalence of microprocessor-based controllers in automotive systems has greatly increased the need for tools which can be used to validate and tune the control systems over their full range of operation. The objective of this paper is to develop a real time simulator for an anti-lock brake system (ABS) based on the methodology of hardware-in-the-loop simulation using a personal computer. By use of this simulator, the analysis of a commercial electronic control unit as well as the validation of the developed control logic for ABS was performed successfully. The idea of the simulator can be applied to the development of more advanced control systems, such as traction control system, vehicle dynamic control system and so forth.

ABSTRACTA collocation-type control variable optimisation method is used to investigate the extent to which the fully active suspension (FAS) can be applied to improve the vehicle electronic stability control (ESC) performance and reduce the braking distance. First, the optimisation approach is applied to the scenario of vehicle stabilisation during the sine-with-dwell manoeuvre. The results are used to provide insights into different FAS control mechanisms for vehicle performance improvements related to responsiveness and yaw rate error reduction indices. The FAS control performance is compared to performances of the standard ESC system, optimal active brake system and combined FAS and ESC configuration. Second, the optimisation approach is employed to the task of FAS-based braking distance reduction for straight-line vehicle motion. Here, the scenarios of uniform and longitudinally or laterally non-uniform tyre–road friction coefficient are considered. The influences of limited anti-lock braking system (ABS) actuator bandwidth and limit-cycle ABS behaviour are also analysed. The optimisation results indicate that the FAS can provide competitive stabilisation performance and improved agility when compared to the ESC system, and that it can reduce the braking distance by up to 5% for distinctively non-uniform friction conditions.

<div class="section abstract"><div class="htmlview paragraph">Amphibious vehicles with both land and water navigation functions have extremely high application value in the military and civilian fields. In order to fully utilize the wheel driving force and ensure the smooth landing of the amphibious vehicle driven by four wheel hub motor, an acceleration slip regulation (ASR) is designed under the condition of landing from water. First, the road friction coefficient is identified based on the back propagation neural network (BPNN). Then, utilizing the improved Burckhardt model, the current optimal slip ratio is calculated from the identified road friction coefficient. Finally, the ASR under the condition of landing from water is designed based on radial basis function (RBF) single neuron adaptive PID control algorithm. By analyzing the process of amphibious vehicles transitioning from water to land, a typical working condition for amphibious vehicles landing is established, and a joint simulation is conducted using CarSim/Simulink. The simulation results indicate that, using the BPNN, the road friction coefficient of the typical working condition can be accurately identified. Compared to without ASR, after applying the ASR designed in this paper, the landing time of the amphibious vehicle is reduced by 7.5 s, and the final climbing velocity is increased by 7 km/h. After applying the ASR, the power performance of the amphibious vehicle during landing is improved, demonstrating that this ASR has significant practical application value.</div></div>