Road Adhesion Coefficient Research Articles

Research on path planning based on new fusion algorithm for autonomous vehicle

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

Drive control strategy of two-wheel independent drive electric vehicle

According to the advantages of two-wheel independent drive electric vehicle that the torque and rotational speed of the drive wheel can be easily measured, a driving control strategy of driving antiskid control and torque coordination control is proposed based on road surface identification slip rate and adhesion coefficient. The principle of road surface identification is given. According to the signals of wheel speed sensor, lateral acceleration, longitudinal acceleration and torque sensor, the wheel slip ratio, road surface adhesion coefficient, vertical force and longitudinal force are calculated. Combined with μ-S function curve proposed by Burckhardt et al., the road surface adhesion coefficient and optimal slip ratio are identified. Driving antiskid control strategy adopts PID control technology based on road surface identification of optimal slip rate, peak adhesion coefficient and optimal slip rate, which mainly includes four parts: establishment of wheel stress model of two-wheel independently driven electric vehicle, approximate calculation of slip rate and road adhesion coefficient, optimal slip rate estimation based on fuzzy control and PID controller design based on optimal slip rate. The torque coordination control adopts PID control method based on ideal yaw rate.

H ∞ Loop Shaping Robust Control for Body Roll of Tractor-semitrailer on Ice-Snow Road

In view of the problems that the poor driving stability and the increase body rolling even instability of the tractor-semitrailer in turns with a middle-speed on the ice-snow road, a robust stably controller that uses a combination method of H ∞ and loop shaping is designed. The tractor-semitrailer dynamics model with the body roll degree of freedom is been established, and the influence of different ice-snow road adhesion coefficients on the dynamic characteristics of body rolling angle is analyzed. Finally, the simulation verification carries out that the controller which is used H ∞ loop shaping can make the body roll of tractor-semitrailer stable, and the controller has a good robust stability.

Model Predictive Controller-Based Optimal Slip Ratio Control System for Distributed Driver Electric Vehicle

The slip ratio control is an important research topic in in-wheel-motored electric vehicles (EVs). Traditional control methods are usually designed for some specified modes. Therefore, the optimal slip ratio control cannot be achieved while vehicles work under various modes. In order to achieve the optimal slip ratio control, a novel model predictive controller-based optimal slip ratio control system (MPC-OSRCS) is proposed. The MPC-OSRCS includes three parts, a road surface adhesion coefficient identifier, an operation mode recognizer, and an MPC based-optimal slip ratio control. The current working road surface is identified by the road surface adhesion coefficient identifier, and a modified recursive Bayes theorem is used to compute the matching degree between current road surfaces and reference road surfaces. The current operation state is recognized by the operation mode recognizer, and a fuzzy logic method is applied to compute the matching degree between actual operation state and reference operation modes. Then, a parallel chaos optimization algorithm (PCOA)-based MPC is used to achieve the optimal control under various operation modes and different road surfaces. The MPC-OSRCS for EV is verified on simulation platform and simulation results under various conditions to show the significant performance.

Nonlinear Model Predictive Control of Autonomous Vehicles Considering Dynamic Stability Constraints

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle performance is increasingly highlighted in many highway driving scenarios, which leads to more priorities to vehicle stability as well as tracking accuracy. In this paper, a nonlinear model predictive controller for autonomous vehicle trajectory tracking is designed and verified through a real-time simulation bench of a virtual test track. The dynamic stability constraints of nonlinear model predictive control (NLMPC) are obtained by a novel quadrilateral stability region criterion instead of the conventional phase plane method using the double-line region. First, a typical lane change scene of overtaking is selected and a new composited trajectory model is proposed as a reference path that combines smoothness of sine wave and comfort of linear functional path. Reference lateral velocity, azimuth angle, yaw rate, and front wheel steering angle are subsequently taken into account. Then, by establishing a nonlinear vehicle dynamics model where Magic Formula of nonlinear tire model is adapted, the quadrilateral vehicle stability region is defined in consideration of designed velocity, road adhesion coefficient, and front wheel steering angle. Working condition-variant constraints determined by the boundaries of the quadrilateral region are subsequently obtained to guarantee the stability and vehicle performance. Finally, a nonlinear motion state space model with measured and unmeasured disturbance for NLMPC tracking maneuver is proposed, Meanwhile, a multi-objective cost function based on track error, ride comfort, and the smoothness of control derivative is established. Laguerre functions are applied to design optimal control trajectory and Hildreth’s quadratic programming procedure is introduced to find converged solutions meeting constraints derived from previously investigated quadrilateral stability region for sake of lightening computation load and finding better numerically conditioned solutions of control when NLMPC is implemented online. The configuration of a real-time virtual test track is explained and the NLMPC algorithm is validated. The simulation and experiment results are illustrated to show the effectiveness of the designed nonlinear model predictive control scheme under the test of the overtaking scene compared with the conventional driver control. This work may provide a useful basis for researches of autonomous vehicle lane change in terms of track accuracy, ride comfort as well as stability.</div></div>

Research of Vehicle Rear-End Collision Model considering Multiple Factors

The accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. The previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). The two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. The research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.

Active Collision Avoidance System Design Based on Model Predictive Control with Varying Sampling Time

In active collision avoidance, the trajectory tracking controller determines the deviation from the reference path and the vehicle stability. The main objective of this study was to reduce the tracking error and improve the tracking performance in collision avoidance. Unlike the previously proposed model predictive control (MPC) strategies with constant sampling time, an improved MPC controller with varying sampling time based on the hierarchical control framework was proposed in this paper. Compared with the original MPC tracking controller, the improved MPC controller demonstrated better adaptive capability for the varying road adhesion coefficients and vehicle speed on a curved road. The simulation results revealed that the hierarchical control framework generated an optimal trajectory for collision avoidance in real-time by minimizing the potential field energy.

Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.

Trajectory Tracking Control for Four-Wheel Independent Drive Intelligent Vehicle Based on Model Predictive Control

For four-wheel independent drive intelligent vehicle, the Model Predictive Control (MPC) is adopted to design the trajectory tracking controller through active steering and four-wheel independent drive/brake. The control objective is to follow a desired trajectory while taking into account the control actuator constraints and vehicle dynamic stability constraints. To reduce the computational complexity, a linear time-varying model predictive controller is designed to linearize the nonlinear vehicle model locally at each sampling point. The co-simulation of CarSim/Simulink shows that the designed controller has high tracking accuracy on the basis of ensuring vehicle stability and strong robustness to vehicle velocity and road adhesion coefficient. The trajectory tracking accuracy based on MPC is better than that of the preview driver model (PDM).

Modeling and Simulation Analysis of Wet Multi-Disk Service Braking System for Heavy Vehicles

A new type of charging valve with electro-hydraulic closed-loop feedback is designed, and a wet multi-disc dual-circuit service brake is built based on AMEsim. The charging and discharging characteristics of the accumulator are studied. The results show that under the condition of a medium-flow constant current source, the dual-circuit accumulator responds quickly, and a single charging can smoothly provide multiple stable discharge output. The response to the pressure output caused by the static structural parameters of the tandem braking valve and the pedal signal is studied. The results show that: the pedal signal can significantly change the brake pressure output, but the preload of the return spring is not apparent; the change of the cross-sectional diameter of the rear axle spool will cause the spool opening lags, and the difference in the spool diameter will cause the spool closing lags. The time response performance of single-axle braking and dual-axle braking is studied. The response from environmental factors such as road adhesion coefficient, slope, load, and brake cool modes to braking distance, braking deceleration, and braking temperature is studied. We establish a parameter ranges of braking safety zone and braking safety comfort zone. This study provides a comparison and reference to the design of a new charging valve and the performance test of the service braking system of heavy vehicles.

Engine-Based Input-Output Linearization for Traction Control Systems

Engine-based traction control marks a paradigm shift for electronic stability systems in the automotive industry. It enables traction control systems with higher bandwidth and performance by an architectural change. As a new approach, only few work exists that considers analytic control design for engine-based traction control. This paper extends our recent work on input-output linearization for engine-based traction control. Global, exponential stability for arbitrary vehicle parameters and time-varying road adhesion coefficients is shown for the first time. Experiments in a test vehicle compare the proposed design with different traction control systems. It is shown that on the considered maneuver, the control design achieves superior tracking performance, disturbance attenuation and damping of drivetrain oscillations.

Design, modeling, and verification of a test bench for braking simulation of 1/4 vehicle

Brake-by-wire systems are one of the key components in intelligent/unmanned vehicles that have attracted worldwide attention. Testing and evaluation of brake-by-wire systems are a significant step during the development of the technology of vehicular braking and further the advancement of intelligent/unmanned vehicles. Using the test bench to simulate different road adhesion coefficients (i.e. road surfaces) and to complete the testing and evaluation of the vehicle braking systems is of great significance and importance. A test bench for simulation of vehicular braking of 1/4 vehicle is presented and investigated in this article. It is composed of a motor, two rollers, a 1/4 vehicle suspension system, a magnetic powder clutch, a flywheel, sensors, a signal acquisition and processing system, and a controller. The wheel and vehicle speeds are simulated by the rollers and flywheel speeds, respectively. The translational kinetic energy of 1/4 vehicle is simulated by the rotational kinetic energy of the flywheel. The signal acquisition and processing system is used to acquire and process the experimental signals, such as rotational speeds and torques during tests. The magnetic powder clutch with adjusted applied currents in the test bench is used to real-timely simulate roads with different adhesion coefficients. Based on the working principle of the test bench and the fundamentals of vehicle dynamics, the prototype of the test bench is established and the simulation approach of the translational kinetic energy of 1/4 vehicle is investigated. The mathematical model of the real-time simulation about roads with different adhesion coefficients based on the magnetic powder clutch with real-time controllable transmitted torque is established. With the built 1/4 vehicle braking systems based on the test bench and road, and the corresponding established models, the comparison and analysis of the simulation results of various road surfaces are conducted. Experiments are sequentially implemented to verify the feasibility and effectiveness of the test bench.

A BP-PID controller-based multi-model control system for lateral stability of distributed drive electric vehicle

The lateral stability is the crucial feature in a distributed drive electronic vehicle (DDEV). A high speed DDEV in a sharp turn may lose the lateral stability when it encounters fast varied road adhesion coefficients. To solve this problem, a BP-PID controller-based multi-model control system (MMCS) is designed for DDEV via direct yaw-moment control (DYC) in this paper. Firstly, according to the varied road adhesion coefficients, the working circumstance of DDEV is summarized as seven kinds of typical types. A sub-model set is established to accurately describe the operating mode of the working circumstance. Secondly, based on the sub-model set, a nonlinear sub-controller set is constructed with seven off-line tuning BP-PID controllers and an on-line tuning one. The off-line tuning controller can fast calculate the required direct yaw-moment, and the on-line tuning controller is aimed to achieve a high control accuracy. Thirdly, a controller switching policy is composed of an error judgement policy and a model matching policy. Such switching policy is utilized to precisely identify the working circumstance of DDEV and implement switching control. Finally, simulation experiments prove that the designed MMCS shows a significant control performance and guarantees the lateral stability of DDEV under varied road adhesion coefficients.

Study on the optimal lane changing steering input under different road adhesion coefficients

To study the influence of road adhesion coefficient on lane changing steering input, vehicle handling inverse dynamics based on hp-adaptive Radau pseudospectral method is proposed in this article. First, a steering inverse dynamics model is established and then the inverse dynamics problem is converted into an optimal control problem. Second, by applying the hp-adaptive Radau pseudospectral method, the optimal control problem is converted into a nonlinear programming problem, which is then solved through sequential quadratic programming. Besides, two contrast verifications are carried out to validate the correctness and advantages of the proposed method. The simulation results show that the obtained control input satisfies the vehicle dynamic constraints. And comparing with Gauss Pseudospectral Method (GPM), hp-adaptive Radau pseudospectral method has higher computational accuracy and computational efficiency when solving non-smooth problem. The study of optimal steering input can provide guidance for driver’s lane changing behavior, so that the accident rate caused by human factors can be decreased. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

Nonlinear Robust Control of Antilock Braking Systems Assisted by Active Suspensions for Automobile

In this brief, a novel robust control of an antilock braking system (ABS) with the assistance of an active suspension system (ASS) is proposed for automobile to improve the braking performance. Different from other ABS, not the reference wheel slip ratio but the peak tire–road adhesion coefficient is taken as the control object, and the barrier Lyapunov function control method is adopted to confine the control object within a smaller and more stable area. Besides, the rolling resistance is taken as an external disturbance and counteracted by a modified controller. Furthermore, the increase of a tire vertical load via the ASS is explored to further shorten the braking distance under the condition that the trajectory of the sprung mass and suspension space is bounded to a predetermined limitation. Finally, a favorable control simulation consequence is obtained, validating the effectiveness of the proposed control scheme.

Multiple Limit Cycles Shimmy of the Dual-Front Axle Steering Heavy Truck Based on Bisectional Road

The shimmy problem causes considerable harm to vehicles and is difficult to solve, especially multiple limit cycle shimmy. Moreover, the dynamic behavior of the multiple limit cycle shimmy of vehicles based on a bisectional road is more complex. Shimmy is practically observed in trucks of cooperative factories during utilization. Thus, we take a heavy truck of a cooperative factory as the prototype and establish a dynamic model of the vehicle-road coupling shimmy system, considering the road adhesion coefficient and dry friction between the suspension and steering system. Based on the dynamic model, the Hopf bifurcation theory is used to qualitatively analyze the existence of the limit cycle for the vehicle shimmy system, and the multiple limit cycle shimmy phenomenon is successfully reproduced using a numerical method. Moreover, the effect of the road adhesion coefficient on the multiple limit cycle shimmy characteristic is studied. Results show that the speed interval and amplitude of the multiple limit cycle shimmy decrease with the road adhesion coefficient; when the coefficient is reduced to a certain extent, the multiple limit cycle shimmy phenomenon is not observed. In addition, the adhesion coefficient of the second axle has a stronger effect on the shimmy characteristic than that of the first axle.

Braking Efficiency and Stability of Chassis Braking System of Combine Harvester: The Theoretical Derivation and Virtual Prototype Simulation

With the advancement of agricultural mechanization, the safety of agricultural vehicles has aroused extensive concern. However, conventional methods evaluate the performance of the combine harvesters in a laborious and inaccurate filed-test way. It is still a challenge to evaluate their performance in a theoretical derivation-based simulation way. Here, we accurately derive the braking model of the combine harvester, which provides a guidance for further braking simulation. Firstly, a four-wheel braking system was designed and theoretically checked. Secondly, the virtual prototype of the chassis braking system was established in ADAMS, in consideration of the complicated contact characteristics between the tire and the road and between the friction pad and the brake disk. Finally, simulation experiments of braking efficiency and directional stability were carried out under different braking conditions. By this means, we find a novel effective yet simple way to optimize the braking efficiency as well as the sufficient braking stability of combine harvesters. The results show that braking efficiency would be improved with stronger braking force, lower initial braking velocity, and lighter weight of the combine harvester. Compared with straight-line braking, steering braking shows lower braking efficiency and less inclination of rear wheel bounce under the same braking conditions. As for braking directional stability, the lateral slippage would be increased with the locking of rear wheels, higher driving speed, or lower road adhesion coefficient. In addition, the simulation results are in agreement with the theoretical results, proving the validity of the virtual prototype simulation. Overall, other than traditional filed-test methods, our method provides an effective yet simple way for designing and evaluating the chassis braking system of combine harvesters.

Study on Dynamic Modeling and Anti-slip Control for Four-wheel Independent Driving/Steering Electric Vehicle

This paper presents a vehicle dynamic model of 7-freedom degrees with different road adhesion coefficients based on MATLAB/Simulink platform. The correctness was verified by the dynamic simulation of vehicle model with the same parameters established in CarMaker software. In order to reduce the torque of the slip wheel to make full use of the road adhesion as well as to regulate the torque of non-slip wheel to ensure the stability of the vehicle, the control strategy of driving anti-slip control system with stability torque adjustment function working in full speed range was designed. In terms of torque distribution, according to different four-wheel slip state, it was proposed to different stability torque adjustment mode. The controller model was set up, and the closed-loop simulation of docking and split-road surface were carried out. The effectiveness of the anti-slip control and the torque control strategy are verified based on the simulation platform.

Integrated control of AFS and DYC for in-wheel-motor electric vehicles based on operation region division

The integrated chassis control system can improve vehicle handling and stability effectively. This paper proposes an integrated control system based on operation region division of active front steering (AFS) and direct yaw moment control (DYC) for in-wheel-motor electric vehicles. The control system adopts a two-layer hierarchical control structure. The decision layer employs a modified sliding mode controller to calculate the required corrective yaw moment, and determines operating regions of the two subsystems based on the driving conditions (road adhesion coefficient, tyre load and wheel slip ratio). The execution layer generates the corrective steer angle and the driving/braking torques for the AFS and DYC subsystems respectively. Simulation results show that on the high-adhesion-coefficient road, the integrated control system appropriately adopts subsystems to improve handling, while attenuating the workload of barking system; on the slippery road, the integrated control system maintains vehicle stability and provides control performance superior to those resulting from the single systems.

An Accurate Vehicle and Road Condition Estimation Algorithm for Vehicle Networking Applications

The Internet of Vehicles is essential for building smart cities. By analyzing the big data collected by vehicle sensors on the road, we can estimate vehicle information and real-time road conditions. To improve the prediction accuracy, this paper proposes a new adaptive filtering algorithm for variable measurement noise problems that occur during the driving state estimations of two-axle electric vehicles. Based on the nonlinear three-degree-of-freedom vehicle model, the dual-motor torque output model, and the Dugoff tire model, fuzzy logic is used to correct the measurement noise in the cubature Kalman filter algorithm. Moreover, the ant colony algorithm is used to optimize the input and output membership functions. Based on the big sensor data, we can accurately predict road conditions, such as vehicle speed and road adhesion coefficients. The simulation results based on CarSim/Simulink show that the new algorithm improves the estimation accuracy of the whole system, regardless of whether the measurement noise is fixed or variable. The research in this paper provides a reference for multi-data comprehensive analyses under different vehicle states.