Road Adhesion Research Articles

Optimal Trajectory Planning and Control for Automatic Lane Change of in Wheel Motor Driving Vehicles on Snow and Ice Roads

In the process of vehicles changing lanes, ice and snow pavement is a very common special working condition, and the research on automatic lane change is usually based on medium and high adhesion coefficient roads. Aiming at the special situation of low adhesion coefficient of ice and snow pavement, this paper studies the process of automatic lane change of vehicles on low adhesion roads driven by the front drive automobile driven by in-wheel motor. First, a dynamic model of a front drive automobile driven by in-wheel motor is established. Then, the trajectory planning is performed based on the fifth-degree polynomial method; the lateral acceleration threshold to ensure the safety of the lane change is deduced; the optimization function of the lane change time and the longitudinal driving displacement is established; and the optimal trajectory to ensure the safety and the lane change efficiency is obtained. Finally, the controller is established based on the fuzzy adaptive PID control algorithm, and the driving torque of the hub motor is distributed through the torque distribution strategy to perform the trajectory tracking control of the vehicle. Simulation results show that the vehicle can safely and smoothly change lanes along the planned trajectory.

Variable gain control-based acceleration slip regulation control algorithm for four-wheel independent drive electric vehicle

In order to improve the dynamic performance and stability of general acceleration slip regulation (ASR) control technology for four-wheel independent drive electric vehicle (4WID EV), an ASR control strategy based on variable gain controller (VGC) is proposed in this paper. First of all, a road identification strategy is designed to identify the current road surface and calculate the optimal slip ratio of the road. Then, the optimal slip ratio is taken as the control target, and the ASR control strategy based on VGC is designed to keeps slip ratio around the optimum slip ratio through controlling the driving torque output, so wheels can make the best of road adhesion to prevent vehicle from slipping. Meanwhile, we analyze the control system state space, and build a scalar function of the system, and prove that the system satisfies Lyapunov large scale asymptotic stability theorem, so the parameters of the VGC does not affect the system stability. Then, in order to meet the requirement of quick dynamic response and no overshoot, parameters selection of VGC is deduced by mathematics. Finally, the co-simulation of Matlab/Simulink and Carsim results show that the proposed control strategy is with the better dynamics and stability, and can better prevent wheel slipping on various roads.

Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

How to enhance and ensure the vehicle yaw/lateral stability and safety performance is a crucial and worthy research problem for the autonomous four in-wheel-motor independent-drive vehicle. In this paper, a hierarchical path tracking control strategy coordinated with the direct yaw moment control for autonomous four in-wheel-motor independent-drive vehicle is proposed with consideration of the lateral stability. In the upper controller, a novel model predictive control method based on the vehicle 7-degree-of-freedom dynamic model is presented to obtain the front wheel steering angle and two virtual generalized forces, the traction and yaw moment. A dynamic adjustment method of target weight based on variance adjustment factor is proposed to realize multi-objective cooperative control of maneuverability, path tracking accuracy, and yaw stability. Moreover, the tire stability margin and its standard deviation are put forward in the objective function of the lower optimal generalized force distributor, making full use of the road adhesion of each wheel. Carsim–Simulink joint simulation results illustrate that the proposed coordinated control strategy could prosper the multi-objective coordinated control. In addition, the path tracking coordinated control strategy could further improve the lateral stability and safety of the vehicle compared with the mere path tracking control strategy, when autonomous four in-wheel-motor independent-drive vehicle is placed under extremely dangerous road conditions.

Lane Departure Assistance Coordinated Control System for In-Wheel Motor Drive Vehicles Based on Dynamic Extension Boundary Decision

Aiming at the advantages in active safety control of in-wheel motor drive vehicles, the classified decision-control-execution is proposed to the lane departure assistance system under the premise of ensuring the stability of the vehicle. The dynamic extension boundaries which vary with vehicle speed, road curvature and road adhesion coefficient are designed in the decision layer by using the neural network algorithm, which divides different dangerous degrees into different decision regions. The active differential steering controller, the electronic differential controller and their coordinated control strategy are designed in the control layer, and the corresponding control is carried out among different decision regions. In order to avoid the dangerous situation of secondary deviation caused by the existence of drift angle when the vehicle is rectified to the lane centerline, the heading angle controller is then designed in the control layer. The different torque distribution modes are divided according to the current vehicle speed, road adhesion coefficient and lateral acceleration in the execution layer, and the economic distribution, stability distribution, joint distribution are correspondingly executed among different torque distribution modes. Finally, Carsim and Matlab/Simulink are used for simulation verification.

A fast model predictive control allocation of distributed drive electric vehicles for tire slip energy saving with stability constraints

This paper proposes a fast model predictive control allocation (MPCA) approach to minimize the tire slip power loss on contact patches for distributed drive electric vehicles (DDEV). In this strategy, two assumptions are set up from a practical focus: (1) the vehicle acceleration and yaw rate are measurable by global position system (GPS)/ inertial navigation system (INS) and inertial measurement unit (IMU), respectively; (2) the longitudinal velocity, road adhesion factor, and vehicle yaw rate are arranged to be “already known” by advanced estimators. For the strategy design, a CarSim-embedded driver model and a linear quadratic regulator (LQR) based direct yaw moment controller, are respectively applied to calculate the desired longitudinal traction and yaw moment as a virtual input first. Then, a MPCA method is proposed to reasonably distribute the virtual input among four in-wheel motors in order to optimize the tire slip power loss and vehicle stability performance. To accurately characterize tire slip power loss in MPCA, a tire slip estimator is established for tire slip information acquirement. Moreover, addressing on the heavily computational challenge in MPCA, a modified continuation/generalized minimal residual (C/GMRES) algorithm is employed. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the barrier functions are applied for transforming the inequality constraints to equivalent cost. According to Pontryagin’s minimum principle (PMP) conditions, the existence and uniqueness for solution of the modified C/GMRES algorithm are strictly proved. Subsequently, a Karush–Kuhn–Tucker​ (KKT) condition based approach is developed to fast gain the optimally initial solution in C/GMRES algorithm for extending application. Finally, numerical simulation validations are implemented and demonstrate that the proposed MPCA can ensure the compatibility between the tire slip power loss reduction and vehicle stability in a computationally efficient way.

A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles.

This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

Research on intelligent vehicle trajectory tracking based on model predictive control

Intelligent vehicle trajectory tracking lateral control is the key technology to realize intelligent driving. Aiming at the problems that the existing control methods have a small effective control area and poor control effect under cornering conditions or small road adhesion coefficients, a model predictive control (MPC) algorithm is proposed for trajectory tracking control. Based on the two-degree-of-freedom vehicle model, a state space equation is established and discretized to obtain a predictive model that meets the conditions. Then, the corresponding objective function is designed based on the ideal yaw angular velocity under the optimal preview control theory, and the input-output constraints and side-slip angle constraints are designed according to the control and state quantities. The controller model is established in MATLAB / Simulink, and the road model is built in CarSim for joint simulation. Compared with CarSim’s own algorithm, the changes in side acceleration and steering wheel angle are smoother and less oscillating. The maximum lateral deviation and lateral acceleration are also smaller, and the lateral stability of the car is improved. It has better adaptability on pavement with lower adhesion coefficient, and can also maintain control stability. The real vehicle test results are consistent with the simulation results, with a maximum lateral error of 0.44m and a maximum heading angle deviation of 1.43 °. Therefore, the trajectory tracking control algorithm based on model predictive control designed in this paper has significantly improved control accuracy and stability, and can maintain control stability even when the road surface adhesion coefficient is low.

Driving force coordinated control of an 8×8 in-wheel motor drive vehicle with tire-road friction coefficient identification

Because of the complexities of tire-road interaction, the wheels of a multi-wheel distributed electric drive vehicle can easily slip under certain working conditions. As wheel slip affects the dynamic performance and stability of the vehicle, it is crucial to control it and coordinate the driving force. With this aim, this paper presents a driving force coordination control strategy with road identification for eight-wheeled electric vehicles equipped with an in-wheel motor for each wheel. In the proposed control strategy, the road identification module estimates tire-road forces using an unscented Kalman filter algorithm and recognizes the road adhesion coefficient by employing the recursive least-square method. According to road identification, the optimal slip ratio under the current driving condition is obtained, and a controller based on sliding mode control with a conditional integrator uses this value for acceleration slip regulation. The anti-slip controller obtains the adjusting torque, which is integrated with the driver-command-based feedforward control torque to implement driving force coordination control. The results of hardware-in-loop simulation show that this control strategy can accurately estimate tire-road forces as well as the friction coefficient, and thus, can effectively fulfill the purpose of driving force coordinated control under different driving conditions.

Extension coordinated control of four wheel independent drive electric vehicles by AFS and DYC

In this paper, a new extension coordinated controller was proposed for driving stability and handling performance of four wheel independent drive electric vehicles. The proposed controller has three levels. The upper control level uses a new extension coordinated controller to find the weights of a active front wheel steering controller (AFS) and a direct yaw moment controller (DYC). Moreover, considering the different vehicle speed, the road adhesion coefficient and the wheel steering angle, a phase plane method was used to provide the dynamic stability boundary for the switching control strategy of AFS and DYC. The medium control level used the triple-step nonlinear method to calculate the additional front wheel angle and additional yaw moment required by the lower control level. The additional front wheel angle and additional yaw moment was applied to the steering motor and the four wheel drive motor, respectively. In order to obtain the target wheel force, the four wheel drive torque was optimized based on a quadratic programming method. The proposed extension coordinated controller was performed in the CarSim/Simulink co-simulation platform, hardware-in-loop (HIL) and vehicle test. The results showed that the proposed controller can effectively improved both the stability and handling performance.

Optimization of Vehicle Braking Distance Using a Fuzzy Controller

The paper presents the study of an anti-lock braking system (ABS) that has been complemented by a fuzzy controller. The fuzzy controller was used to improve the braking performance of the vehicle, particularly in critical situations, for example, when braking a vehicle on wet road. The controller for the ABS was designed in the MATLAB/Simulink program. The designed controller was simulated on a medium-size vehicle model. During testing, three braking systems were simulated on the vehicle model. We compared the performance of a braking system without an ABS, a system with a threshold-based conventional ABS, and a braking system with the proposed ABS with a fuzzy controller. These three braking systems were simulation tested during braking the vehicle on a dry straight road and on a road with combined road adhesion. A maneuverability test was conducted, where the vehicle had to avoid an obstacle while braking. The results of each test are provided at the end of the paper.

Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope

ABSTRACT In the design of a path-tracking controller for autonomous vehicles, mediating the conflicting objectives among path-tracking, vehicle stabilisation, and low computational cost is a challenging issue. Accordingly, this paper proposes a model predictive control (MPC)-based path-following controller with steering angle envelopes. In contrast to previous MPC-based structure, in which the constraints in terms of the road sides and lateral stabilisation directly impose on the algorithm, in the proposed approach, these aspects are formulated as the steering angle envelopes. This feature enables the controller to simultaneously ensure the tracking performance and computational feasibility. Moreover, considering the trade-off between path-tracking and vehicle stabilisation, the full-length prediction horizon pertaining to the steering angle envelopes are divided into two different parts: the safe road envelope is applied in the short-term, and the stable handling envelope is applied in other part. By modelling vehicles driving under different road conditions with various driving speeds and road adhesion values, the developed controller is compared with several existing control schemes via simulations and hardware-in-the-loop (HIL) experiments. The comparison results demonstrate the effectiveness of the proposed controller in realising path-tracking, lateral stabilisation, and real-time computational capabilities in the best possible manner.

Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking

This paper investigates a slip ratio control problem of a vehicle braking system in which real-time road conditions are unavailable. To overcome the nonlinearity and uncertainties, the slip ratio control problem is converted to a speed tracking control problem. By introducing a road-condition observer, a model reference adaptive control (MRAC) method is proposed to overcome modeling errors and system uncertainties. The road condition observer based on the Lyapunov theory is designed and a sliding mode observer is also constructed to estimate the unknown vehicle velocity. The asymptotic and robustness property of the adaptive controller is derived by the Popov hyperstability theory. Simulations under different conditions including different road adhesion coefficient, system uncertainties and modeling errors are provided to illustrate the effectiveness and robustness of the proposed method.

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.