A curving ACC system with coordination control of longitudinal car-following and lateral stability

The paper presents a curving adaptive cruise control (ACC) system that is coordinated with a direct yaw-moment control (DYC) system and gives consideration to both longitudinal car-following capability and lateral stability on curved roads. A model including vehicle longitudinal and lateral dynamics is built first, which is as discrete as the predictive model of the system controller. Then, a cost function is determined to reflect the contradictions between vehicle longitudinal and lateral dynamics. Meanwhile, some I/O constraints are formulated with a driver permissible longitudinal car-following range and the road adhesion condition. After that, desired longitudinal acceleration and desired yaw moment are obtained by a linear matrix inequality based robust constrained state feedback method. Finally, driver-in-the-loop tests on a driving simulator are conducted and the results show that the developed control system provides significant benefits in weakening the impact of DYC on ACC longitudinal car-following capability while also improving lateral stability.     

基于预测恒定车头时距策略的自适应巡航控制研究

为了进一步提高车辆跟车过程中的跟踪性、安全性、舒适性和燃油经济性，针对已有间距策略表现过于保守或反应过于激烈等不足之处，提出了一种预测恒定车头时距策略。该策略考虑了相对加速度，建立了一种预测型期望车间距模型，进而应用于模型预测控制的多目标自适应巡航控制系统中，能进一步提高模型预测控制对多个控制目标的综合协调能力。搭建上层控制器、下层PID控制器、油门制动切换、逆纵向动力学模型。在多工况下仿真，通过建立性能评判指标对多目标进行量化分析。结果表明，所提出的间距策略在保证安全性的前提下，提升了自适应巡航控制系统的综合性能。在不同驾驶风格的车头时距下，跟踪性、舒适性和燃油经济性均有良好表现。     

Robust yaw stability control for electric vehicles based on active front steering control through a steer-by-wire system

A robust yaw stability control design based on active front steering control is proposed for in-wheel-motored electric vehicles with a Steer-by-Wire (SbW) system. The proposed control system consists of an inner-loop controller (referred to in this paper as the steering angle-disturbance observer (SA-DOB), which rejects an input steering disturbance by feeding a compensation steering angle) and an outer-loop tracking controller (i.e., a PI-type tracking controller) to achieve control performance and stability. Because the model uncertainties, which include unmodeled high frequency dynamics and parameter variations, occur in a wide range of driving situations, a robust control design method is applied to the control system to simultaneously guarantee robust stability and robust performance of the control system. The proposed control algorithm was implemented in a CaSim model, which was designed to describe actual in-wheel-motored electric vehicles. The control performances of the proposed yaw stability control system are verified through computer simulations and experimental results using an experimental electric vehicle.     

Connected cruise control: modelling,delay effects,and nonlinear behaviour

Connected vehicle systems (CVS) are considered in this paper where vehicles exchange information using wireless vehicle-to-vehicle (V2V) communication. The concept of connected cruise control (CCC) is established that allows control design at the level of individual vehicles while exploiting V2V connectivity. Due to its high level of modularity the proposed design can be applied to large heterogeneous traffic systems. The dynamics of a simple CVS is analysed in detail while taking into account nonlinearities in the vehicle dynamics as well as in the controller. Time delays that arise due to intermittencies and packet drops in the communication channels are also incorporated. The results are summarised using stability charts which allow one to select control gains to maintain stability and ensure disturbance attenuation when the delay is below a critical value.     

Handling stability improvement through robust active front steering and active differential control

The design of the integrated active front steering and active differential control for handling improvement of road vehicles is undertaken. The controller design algorithm is based on the solution of a set of linear matrix inequalities that guarantee robustness against a number of vehicle parameters such as speed, cornering and braking stiffnesses. Vehicle plane dynamics are first expressed in the generic linear parameter-varying form, where the above-stated parameters are treated as interval uncertainties. Then, static-state feedback controllers ensuring robust performance against changing road conditions are designed. In a first series of simulations, the performance of the integrated controller is evaluated for a fishhook manoeuvre for different values of road adhesion coefficient. Then, the controller is tested for an emergency braking manoeuvre executed on a split-μ road. In all cases, it is shown that static-state feedback controllers designed by the proposed method can achieve remarkable road handling performance compared with uncontrolled vehicles.     

汽车纵向加/减速度多模型分层切换控制

针对汽车纵向动力学模型的大不确定性,设计了一种基于鲁棒控制理论的汽车纵向加速度多模型分层切换控制系统。通过分析汽车纵向动力学特性,用4个不确定模型覆盖对象不确定性,并应用LM I方法设计对应的鲁棒性能控制器集合。考虑鲁棒控制系统的特点,设计了一种对不确定性的系统增益进行估计的切换指标函数,以选择控制器进行控制。实验表明,提出的方法在大不确定性下可以对纵向加速度有效控制。     

网联通信时延下的混合队列控制特性分析

针对CACC协同自适应巡航控制技术,探究其在车联网通信时延影响下,与驾驶员驾驶汽车共存而构成的混合队列系统的性能.从微观跟车行为角度,基于频域传递函数,推导通信时延下的CACC队列稳定最小跟车时距的理论表达式,并通过数值验证指出CACC队列稳定最小跟车时距随通信时延增大而增大的特性.从交通激波特性角度,针对无时延CAC...     

Full drive-by-wire dynamic control for four-wheel-steer all-wheel-drive vehicles

Most of the controllers introduced for four-wheel-steer (4WS) vehicles are derived with the assumption that the longitudinal speed of the vehicle is constant. However, in real applications, the longitudinal speed varies, and the longitudinal, lateral, and yaw dynamics are coupled. In this paper, the longitudinal dynamics of the vehicle as well as its lateral and yaw motions are controlled simultaneously. This way, the effect of driving/braking forces of the tires on the lateral and yaw motions of the vehicle are automatically included in the control laws. To address the dynamic parameter uncertainty of the vehicle, a chatter-free variable structure controller is introduced. Elimination of chatter is achieved by introducing a dynamically adaptive boundary layer thickness. It is shown via simulations that the proposed control approach performs more robustly than the controllers developed based on dynamic models, in which longitudinal speed is assumed to be constant, and only lateral speed and yaw rate are used as system states. Furthermore, this approach supports all-wheel-drive vehicles. Front-wheel-drive or rear-wheel-drive vehicles are also supported as special cases of an all-wheel-drive vehicle.     

Application of Analytic Hierarchy Process to Stochastic Robustness Synthesis of Integrated Vehicle Controllers

This paper deals with the robust design procedure of integrated vehicle dynamics controller based on Stochastic Robustness Synthesis with use of a rational decision making process of the controller parameters. The basic control structure that integrates four-wheel steering and four-wheel torque control is determined using a nonlinear predictive control theory. The Analytic Hierarchy Process, a basic approach to decision making, is applied to determine the weight coefficients of robustness evaluation function of the controller. The desired vehicle dynamic performance is described as four-layer hierarchy structure and the design priority is determined with respect to several design criteria. The proposed design process produced a control system with excellent stability and performance robustness to vehicle parameter variations.     

Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle’s dynamics and improve stability and curving performance. A half-car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H_∞ control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball-screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second-order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one-car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.     

Application of Analytic Hierarchy Process to Stochastic Robustness Synthesis of Integrated Vehicle Controllers

This paper deals with the robust design procedure of integrated vehicle dynamics controller based on Stochastic Robustness Synthesis with use of a rational decision making process of the controller parameters. The basic control structure that integrates four-wheel steering and four-wheel torque control is determined using a nonlinear predictive control theory. The Analytic Hierarchy Process, a basic approach to decision making, is applied to determine the weight coefficients of robustness evaluation function of the controller. The desired vehicle dynamic performance is described as four-layer hierarchy structure and the design priority is determined with respect to several design criteria. The proposed design process produced a control system with excellent stability and performance robustness to vehicle parameter variations.     

Lateral dynamics emulation via a four-wheel steering vehicle

In this paper, we examine the lateral dynamics emulation capabilities of an automotive vehicle equipped with four-wheel steering. We first demonstrate that the lateral dynamics of a wide range of vehicles can be emulated, either with little or with no modification on the test vehicle. Then we discuss a sliding mode controller for active front and rear wheel steering, in order to track some given yaw rate and side-slip angle. Analytically, it is shown that the proposed controller is robust to plant parameter variations by±10%, and is invariant to unmeasurable wind disturbance. The performance of the sliding mode controller is evaluated via computer simulations to verify its robustness to vehicle parameter variations and delay in the loop, and its insensitivity to wind disturbance. Finally, the emulation of a bus, a van, and two commercially available passenger vehicles is demonstrated in an advanced nonlinear simulator.     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.     

自适应巡航及协同式巡航对交通流的影响分析

自适应巡航（ACC）和协同式自适应巡航（CACC）等自动驾驶技术正逐渐进入市场,未来一段时间内道路交通流将由人工驾驶车辆与不同等级、不同形式的自动驾驶车辆混合构成。为分析ACC和CACC对交通流的影响,利用实测交通数据NGSim建立人工驾驶车辆跟驰模型,并在综合已有ACC和CACC模型的基础上,提出基于安全间距的自动驾驶跟驰行为模型,进而得出不同ACC,CACC车辆渗透率下交通流的基本图模型。研究结果表明：自动驾驶可以提升交通容量;与ACC车辆比例r_a相比,CACC车辆比例r_c对交通容量的影响更为显著;当r_c>0.5时,饱和流量快速增加,当r_c=1时,饱和流量约为纯人工驾驶时的2倍。进一步,通过仿真考察车辆在车队中的跟驰响应和交通流在瓶颈处的运行情况。研究结果表明：自动驾驶改善了交通流的动态特性,对存在跟驰关系的连续车流来说,自动驾驶使得后车可以更加及时地响应前车的行为,车流会在更短的时间内进入稳态;在交通瓶颈处,自动驾驶降低了拥堵程度,提高了阻塞发生的临界流量。总体来看,自动驾驶对交通流静态和动态性能均有所提升,特别是在协同式自动驾驶场景下,车辆行为更加协调一致,交通流表现出良好的抗扰性,进一步验证了车路协同对自动驾驶的意义。     

Real-time weighted multi-objective model predictive controller for adaptive cruise control systems

In this paper, a novel spacing control law is developed for vehicles with adaptive cruise control (ACC) systems to perform spacing control mode. Rather than establishing a steady-state following distance behind a newly encountered vehicle to avoid collision, the proposed spacing control law based on model predictive control (MPC) further considers fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized in which a lower controller compensates for nonlinear longitudinal vehicle dynamics and enables to track the desired acceleration. The upper controller based on the proposed spacing control law is designed to compute the desired acceleration to maintain the control objectives. Moreover, the control objectives are then formulated into the model predictive control problem using acceleration and jerk limits as constrains. Furthermore, due to the complex driving conditions during in the transitional state, the traditional model predictive control algorithm with constant weight matrix cannot meet the requirement of improvement in the fuel economy and ride comfort. Therefore, a real-time weight tuning strategy is proposed to solve time-varying multi-objective control problems, where the weight of each objective can be adjusted with respect to different operating conditions. In addition, simulation results demonstrate that the ACC system with the proposed real-time weighted MPC (RW-MPC) can provide better performance than that using constant weight MPC (CW-MPC) in terms of fuel economy and ride comfort.     

四轮独立驱动电动车自适应巡航滑模控制

为实现四轮独立驱动电动汽车的自适应巡航功能,采用基于趋近律的滑模控制理论设计了自适应巡航控制系统.上位控制器以实际车距与期望车距的偏差作为输入,采用滑模控制律获得主车期望加速度,然后将期望加速度作为下位控制器的输入,计算出电机期望转矩,用于实现自适应巡航控制.在CarSim中建立电动汽车整车模型,并与Simulink进...     

Modeling and control of an anti-lock brake and steering system for cooperative control on split-mu surfaces

The brake and steering systems in vehicles are the most effective actuators that directly affect the vehicle dynamics. In general, the brake system affects the longitudinal dynamics and the steering system affects the lateral dynamics; however, their effects are coupled when the vehicle is braking on a non-homogenous surface, such as a split-mu road. The yaw moment compensation of the steering control on a split-mu road is one of the basic functions of integrated or coordinated chassis control systems and has been demonstrated by several chassis suppliers. However, the disturbance yaw moment is generally compensated for using the yaw rate feedback or using wheel brake pressure measurement. Access to the wheel brake pressure through physical sensors is not cost effective; therefore, we modeled the hydraulic brake system to avoid using physical sensors and to estimate the brake pressure. The steering angle controller was designed to mitigate the non-symmetric braking force effect and to stabilize the yaw rate dynamics of the vehicle. An H-infinity design synthesis was used to take the system model and the estimation errors into account, and the designed controller was evaluated using vehicle tests.     

Lateral handling improvement with dynamic curvature control for an independent rear wheel drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.     

基于REINFORCE算法和神经网络的无人驾驶车辆变道控制

针对无人驾驶车辆变道超车场景,研究基于REINFORCE算法和神经网络技术的无人驾驶车辆变道控制策略.通过车辆动力学模型确定模型的反馈量、控制量和输出限幅要求;设计神经网络控制器的结构,根据REINFORCE算法设计控制器训练方案;分析经验池数据数值和方差过大的问题,提出1种经验池数据预处理的方法以改进控制器训练方案;...     

Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.