Yaw stability control of a steer-by-wire equipped vehicle via active front wheel steering

A novel yaw stability control algorithm with active front wheel steering control of a vehicle equipped with a steer-by-wire system is presented in this paper. The proposed algorithm achieves the decoupling of the lateral and yaw motion of a vehicle and the vehicle’s yaw damping simultaneously by the feedback of both yaw rate and front steering angle. A trade-off is then made between the robust decoupling and yaw rate damping through the adjustment of the feedback gains with respect to vehicle speed. With this trade-off, the gain scheduled steering controller provides the desired yaw rate damping while keeping the yaw-lateral motion decoupled. The robustness of the yaw-lateral decoupling is achievable when arbitrary yaw damping is not required. The proposed control system is implemented on a steer-by-wire vehicle, and the experimental results are presented illustrating the benefits.     

The use of GPS for vehicle stability control systems

This paper presents a method for using global positioning system (GPS) velocity measurements to improve vehicle lateral stability control systems. GPS can be used to calculate the sideslip angle of a vehicle without knowing the vehicle model. This measurement is combined with other traditional measurements to control the lateral motion of the vehicle. Noise estimates are provided for all measurement systems to allow the sensors to be accurately represented. Additionally, a method to calculate the lateral forces at the tires is presented. It is shown that the tire estimation algorithm performs well outside the linear region of the tire. Results for the controller and force calculations are shown using a nonlinear model to simulate the vehicle and the force calculations are validated with experimental measurements on a test vehicle.     

Development of Steering Control System for Autonomous Vehicle Using Geometry‐Based Path Tracking Algorithm

In this paper, a steering control system for the path tracking of autonomous vehicles is described. The steering control system consists of a path tracker and primitive driver. The path tracker generates the desired steering angle by using the look‐ahead distance, vehicle heading, and a lateral offset. A method for applying an autonomous vehicle to path tracking is an advanced pure pursuit method that can reduce cutting corners, which is a weakness of the pure pursuit method. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. We designed a steering controller that is applied to a proportional integral differential controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome the dead band, a dead band compensator was developed. As a result of the compensator, the path tracking performance and stability are improved.     

Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment

A significant challenge in electric vehicles with multiple motors is how to control the individual drivetrains in order to achieve measurable benefits in terms of vehicle cornering response, compared to conventional stability control systems actuating the friction brakes. This paper presents a direct yaw moment controller based on the combination of feedforward and feedback contributions for continuous yaw rate control. When the estimated sideslip exceeds a pre-defined threshold, a sideslip-based yaw moment contribution is activated. All yaw moment contributions are entirely tunable through model-based approaches, for reduced vehicle testing time. The purpose of the controller is to continuously modify the vehicle understeer characteristic in quasi-static conditions and increase yaw and sideslip damping during transients. Skid-pad, step-steer and sweep steer tests are carried out with a front-wheel-drive fully electric vehicle demonstrator with two independent drivetrains. The experimental test results of the electric motor-based actuation of the direct yaw moment controller are compared with those deriving from the friction brake-based actuation of the same algorithm, which is a major contribution of this paper. The novel results show that continuous direct yaw moment control allows significant “on-demand” changes of the vehicle response in cornering conditions and to enhance active vehicle safety during extreme driving maneuvers.     

Gain-scheduled robust control for lateral stability of four-wheel-independent-drive electric vehicles via linear parameter-varying technique

This paper proposes a robust gain-scheduled H_∞ controller for lateral stability control of four-wheel-independent-drive electric vehicles via linear parameter-varying technique. The controller aims at tracking the desired yaw rate and vehicle sideslip angle by controlling the external yaw moment. In the design of controller, uncertain factors such as vehicle mass and tire cornering stiffness in vehicle lateral dynamics are represented via the norm-bounded uncertainty. To address the importance of time-varying longitudinal velocity for vehicle lateral stability control, a linear parameter-varying polytopic vehicle model is built, and the built vehicle model depends affinely on the time-varying longitudinal speed that is described by a polytope with finite vertices. In order to reduce conservative, the hyper-rectangular polytope is replaced by a hyper-trapezoidal polytope. Simultaneously, the quadratic D-stability is also applied to improve the transient response of the closed-loop system. The resulting gain-scheduling state-feedback controller is finally designed, and solved utilizing a set of linear matrix inequalities derived from quadratic H_∞ performance and D-stability. Simulations using Matlab/Simulink-Carsim® are carried out to verify the effectiveness of the proposed controller with a high-fidelity, CarSim®, full-vehicle model. It is found from the results that the robust gain-scheduled H_∞ controller suggested in this paper provides improved vehicle lateral stability, safety and handling performance.     

Robust energy-to-peak sideslip angle estimation with applications to ground vehicles

In this paper, the observer design problem for the sideslip angle of ground vehicles is investigated. The sideslip angle is an important signal for the vehicle lateral stability, which is not measurable by using an affordable physical sensor. Therefore, we aim to estimate the sideslip angle with the yaw rate measurements by employing the vehicle dynamics. The nonlinear lateral dynamics is modeled firstly. As the tyre model is nonlinear and the road adhesive coefficient is subject to a large variation, the nonlinear lateral dynamics is transformed into an uncertain model. Considering the variation of longitudinal velocity, an uncertain linear-parameter-varying (LPV) system is obtained. Based on the LPV model, a gain-scheduling observer is proposed and the observer gain can be determined with off-line computation and on-line computation. The off-line computation includes the calculation of a set of linear matrix inequalities and the on-line computation contains several algebraic operations. The proposed design methodology is applied to a four-wheel-independent-drive electric vehicle in simulation. It infers from different maneuvers that the designed observer has a good performance on estimating the sideslip angle.     

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.     

Controller design and analysis for automatic steering of passenger cars

This paper deals with a lateral controller design for active 2WS vehicles in ITS applications. First, the vehicle dynamics for typical lateral maneuvers are rigorously described by Newton's formulation. Next, based on a linear perturbed model, a robust controller via μ (mu)-synthesis is designed. Due to the characteristics of D–K iteration, the resulting high-order controller has to be reduced for implementation. Then the frequency- and time-domain responses of the robust controller are extensively evaluated through numerical simulations. Further, we present performance comparisons between full-order controller and reduced-order controller. Finally, the automated steering control presented here is shown to be robust to the prescribed levels of uncertainty for highway maneuvers.     

Integrated model predictive control and velocity estimation of electric vehicles

In this paper, an integrated estimation and control system is developed for the stability and traction control of electric vehicles. A model predictive control technique is used to track the desired vehicle yaw rate while maintaining small lateral velocity and tire slip ratios. This paper proposes a new method to control the lateral stability of the vehicle. In this method, the lateral vehicle velocity is controlled indirectly by adjusting the reference yaw rate. This reduces the size of the prediction model and its computational complexity. The controller requires the vehicle’s lateral and longitudinal velocities as well as its tire forces for stability and traction control. This paper also proposes a novel velocity estimation scheme that uses the combined vehicle kinematics and tire model. The developed Kalman-based estimator provides velocities and lateral forces at each corner that are robust to changes in the road condition. The combined model-based and kinematic-based estimation structure mitigates some common problems of the widely used kinematic-based estimators such as the spikes and drifting issues. The stability of the proposed time-varying estimator is also investigated. The designed control and estimation scheme are experimentally validated on various driveline configurations and proven to provide reliable results.     

Comparison between braking and steering yaw moment controllers considering ABS control aspects

Two yaw motion control systems that improve a vehicle lateral stability are proposed in this study: a braking yaw motion controller (BYMC) and a steering yaw motion controller (SYMC). A BYMC controls the braking pressure of the rear inner wheel, while a SYMC steers the rear wheels to allow the yaw rate to track the reference yaw rate. A 15 degree-of-freedom vehicle model, simplified steering system model, and driver model are used to evaluate the proposed BYMC and SYMC. A robust anti-lock braking system (ABS) controller is also designed and developed. The performance of the BYMC and SYMC are evaluated under various road conditions and driving inputs. They reduce the slip angle when braking and steering inputs are applied simultaneously, thereby increasing the controllability and stability of the vehicle on slippery roads. The SYMC performs better than the BYMC because the SYMC vehicle has four-wheel steering. However, both the BYMC and SYMC vehicles show improved performance during lane-change maneuvers.     

Trajectory tracking of redundantly actuated mobile robot by MPC velocity control under steering strategy constraint

Four-wheel independent-steering redundantly-actuated omnidirectional mobile robot (FIR-OMR) has attracted wide attention for its excellent motion performance such as multi-mode steering and flexible attitude adjustment. However, for the 8-drive redundant and strong nonlinear system of FIR-OMR, the complexity and time-consuming of model solutions make it hard for the embedded system to achieve fast and efficient trajectory tracking. To copy with that, an optimal velocity model predictive control under steering strategy constraint (USSC-MPC) is presented, which is combined with pose correction by local micro error (LME-PC) for independent wheel angle feedback control. Specifically, the steering modes of FIR-OMR are divided into fixed independent wheel steering (IWS), zero-angle pure differential steering (DS), and Ackerman steering (AS), which is ultimately selected by steering fuzzy selector (SFS). Then a steer-based model prediction controller is designed to optimize the wheel velocity and the micro-error feedback wheel angle of pose correction pointing to the desired point is calculated by FIR-OMR in real-time. As a result, by controlling the wheel velocity and wheel angle, a satisfactory path tracking control effect can be obtained. In our proposed method, the USSC-MPC ensures the optimality of wheel velocity, and the LME-PC can achieve efficient trajectory tracking with a faster error convergence rate. Through simulations and experiments, complex motions such as multi-section paths and side parking can be realized, which verifies the feasibility and superiority of our method.     

基于转角的商用车电动助力转向回正控制研究

以改善商用车电动助力转向系统的回正特性为目标,开发了一种基于转向盘转角的回正目标电流控制器,该控制器以转角传感器LH3获取的转角信号为输入,经PID控制方式获取回正目标电流,通过控制助力电机动作,使转向盘准确快速回位。并利用Simulink仿真软件搭建了该回正控制器仿真模型,对回正特性进行分析。仿真结果表明:转向盘的回正稳定时间大大缩短,该回正控制器明显提高了EPS系统的回正性能。     

Modeling of vehicle dynamics from real vehicle measurements using a neural network with two-stage hybrid learning for accurate long-term prediction

This paper describes a neural network (NN) model of a real vehicle and the associated hybrid learning scheme. The NN vehicle models the actual vehicle dynamic behavior with the architecture of a real-time recurrent network. The NN was trained to predict the next state of the vehicle, given the current vehicle state, the current input steering angle of the front wheel, and the vehicle's speed. A hybrid training scheme for the network has been proposed, which consists of two phases: open-loop training for stabilization of the NN weight learning and closed-loop training for long-term prediction of the vehicle behavior. The open-loop training is necessary to avoid learning instability at initial stages. The closed-loop training then follows in such a way that the NN correctly predicts the vehicle's next state in a recursive mode. The outcome is that the model can correctly generate the vehicle trajectory, given the initial state and the steering and speed sequence of the vehicle. Furthermore, after this training procedure, it not only learns the vehicle's lateral dynamics along the trained trajectories, but can also generalize to similar trajectories. This modeling technique has been successfully applied to model the actual dynamics of a Daewoo Leganza vehicle. It is an intelligent vehicle that is fully autonomous in that steering, braking, and accelerating were all done via computer control. The training data were taken from a four-vehicle platoon demonstration in which four vehicles were automatically controlled in a convoy mode.     

基于模糊滑模控制的车辆稳定性研究

针对车辆在转弯或变道引起的车辆稳定性控制问题,建立了用于稳定性控制的3自由度非线性动力学模型。文中将车辆横摆角速度、质心侧偏角作为主要控制变量。基于模糊滑模控制理论,采用直接横摆力矩控制方法控制横摆角速度和质心侧偏角,其中考虑到质心侧偏角难以通过传感器测量,设计了基于递归最小二乘法的质心侧偏角估计方法。同时,在基于Matlab与ADMAS联合调试的环境下进行了仿真分析,仿真结果表明该控制器能有效地使横摆角速度和质心侧偏角跟踪其期望值,使汽车保持在安全稳定的范围内。     

MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

This paper focuses on yaw stability control of in-wheel-motored electric vehicle (EV), and a model predictive controller is designed based on holistic control structure via active front steering and motor torque distribution. By designing a suitable reference model, the controller stabilizes a vehicle along the desired states while rejecting skid and fulfilling its physical constraints, so this is described as a constrained tracking problem. To solve this, the holistic control scheme is built to simplify the hierarchical structure of the controller and directly optimize the control inputs of system. Based on holistic control structure and MPC method, an objective function with constraints is designed over a receding horizon to meet the control requirements. Finally, the proposed nonlinear model predictive controller is evaluated on eight degrees of freedom (8DOF) EV model offline simulation platform. Simulation results of different road maneuver on slippery surfaces show the benefits of the control methodology used.     

Estimation of sideslip angle and cornering stiffness of an articulated vehicle using a constrained lateral dynamics model

In this paper, a constrained lateral dynamics model of articulated vehicles and an algorithm for estimating sideslip angle and cornering stiffness are proposed. The articulated vehicle was modeled using the bicycle model, linear tire model, and modified Dug-off model. The normal force of each axle included in the model was estimated based on the longitudinal load transfer model. Physical constraints were applied to reduce model states. Accurate sideslip angle and cornering stiffness are essential for vehicle control safety and autonomous driving performance. The sideslip angle and cornering stiffness were simultaneously estimated using a dual linear time-varying (LTV) Kalman filter. The observability matrix guaranteed the convergence of the proposed estimation algorithm. The estimation performance was verified by simulation with TruckSim and an experiment using an articulated bus.     

Stabilization and path following of a single wheel robot

We have developed a single wheel, gyroscopically stabilized robot. This is a novel concept for a mobile robot that provides dynamic stability for rapid locomotion. The robot is a sharp-edged wheel actuated by a spinning flywheel for steering and a drive motor for propulsion. The spinning flywheel acts as a gyroscope to stabilize the robot and it can be steered by tilting. This robot is nonholonomic in nature, underactuated and inherently unstable in the lateral direction. In this paper, we first develop a three-dimensional (3-D) nonlinear dynamic model and investigate the dynamic characteristics of the robot. We conduct simulations and real-time experiments to verify the model. Both simulations and experiments show that the flywheel has a significant stabilizing effect on the robot. Then, we can decouple the longitudinal and lateral motions of the robot by linearization. We propose a linear state feedback to stabilize the robot at different lean angles, so as to control the steering velocity of the robot indirectly, because the robot steers only by leaning itself to a predefined angle. For the task of path following, we design a controller for tracking any desired straight line without falling. In the controller, we first design the linear and steering velocities for driving the robot along the desired straight line by controlling the path curvature. We then apply the linear state feedback to stabilize the robot at the predefined lean angle such that the resulting steering velocity of the robot converges to the given steering velocity. This work is a significant step toward fully autonomous control of such a dynamically stable but statically unstable system.     

Modeling and control of steering system of heavy vehicles for automated highway systems

This work presents modeling, analysis, and controller design of the steering subsystem of heavy vehicles as a subsystem of vehicle lateral control system for the automated highway systems. A physical model of the steering subsystem is derived where the hydraulic power assist unit is modeled as a family of static nonlinear boost curves. Based on open-loop frequency tests and analysis of the physical model structure and its dynamical characteristics, a nominal second order linear model of the steering subsystem is obtained. Then, a linear robust loop-shaping controller is designed to provide a good tracking performance of the closed-loop dynamics of the steering subsystem for varying gain cross over frequencies which is a result of the nonlinear characteristics of the hydraulic power assist. The controller has been successfully incorporated as an inner-loop controller into the nested lateral control architecture for autonomous driving and its efficacy has been demonstrated experimentally.