Vehicle trajectory challenge in predictive active steering rollover prevention

High center of mass vehicles are likely to rollover in extreme maneuvers. Available works present control strategies to prevent rollover. In these works, however, other important parameters such as path trajectory tracking are not a main concern. In this paper conflicts between rollover prevention and trajectory tracking is investigated. Model predictive control (MPC) is adopted to predict and avoid rollover while tracking desired trajectory. For this regard a model based future error estimation is introduced. The control framework predicts both rollover and trajectory error simultaneously. It avoids rollover while tries to track the trajectory. Simulation results for two controllers with and without trajectory tracking are presented. The results indicate that the controllers effectively limit rollover as a hard constraint while the trajectory tracking controller also minimizes and recovers the path error.     

Real-time model predictive control of connected electric vehicles

ABSTRACT

Electric Vehicles (EVs) motors develop high torque at low speeds, resulting in a high rate of acceleration with the added advantage of being fitted with smaller gearboxes. However, a rapid rise of torque in EVs fitted with central drive powertrains can create undesired torsional oscillations, which are influenced by wheel slip and flexibility in the halfshaft. These torsional oscillations in the halfshaft lead to longitudinal oscillations in the vehicle, thus creating problems with regard to comfort and drivability. The significance of using wheel slip in addition to halfshaft torsion for design of anti-jerk controllers for EVs has already been highlighted in our previous research. In this research, we have designed a look-ahead model predictive controller (LA-MPC) that calculates the required motor torque demand to meet the dual objectives of increased traction and anti-jerk control. The designed LA-MPC will improve drivability and energy consumption in connected EVs. The real-time capability of the LA-MPC has been demonstrated through hardware-in-the-loop experiments. The performance of the LA-MPC has been compared to other controllers presented in the literature. A validated high-fidelity longitudinal-dynamics model of the Rav4EV, which is the test vehicle of our research has been used to evaluate the controller.     

Hierarchical distributed coordination strategy of connected and automated vehicles at multiple intersections

In this paper, we present a hierarchical distributed coordination strategy for connected and automated vehicles (CAVs) that are travelling through multiple unsignalized intersections. The control strategy focuses on the improvement of vehicle fuel efficiency and system mobility. In presence of wireless communication among the involved CAVs and the intersection controllers, our coordination strategy focuses on leading the CAVs travel through a road network without conventional traffic light control and ensuring collision avoidance at the intersection areas. We propose a three-layered coordination strategy in this paper. First, we evaluate the road desired average velocity considering both upstream and downstream traffic to speed up the traffic density balance. Second, the intersection controllers optimally assign reference velocity to each vehicle based on the minimization of velocity deviation from its current velocity and collision avoidance at the intersections. Finally, fast model predictive control (F-MPC) is applied for each vehicle to track their reference velocity in a computationally efficient manner. Two simulation scenarios with different difficulty levels have been implemented on a two-interconnected intersection network. Simulation results indicate the feasibility and scalability of the proposed method, as well as vehicle fuel efficiency and system mobility improvement.     

空中交通管制员应急处置能力评测

为提高空中交通管制员的应急处置能力,保证航空器在各种紧急情况下的安全运行,基于"洋葱模型",结合管制员的工作特点,通过引入管制员个人特质和意识2个指标,建立"管制员洋葱模型".依据空中交通管制应急处置过程,构建由知识、技能、意识、个人特质4个一级指标和16个二级指标组成的管制员应急处置能力评测指标集,并利用层次分析法得到各指标的权重.运用模糊综合评价模型对某空管单位的管制员应急处置能力进行评测,得出其应急处置能力分数为3.689,即应急处置能力等级为中等.管制员应急处置能力的定量化评测,能够客观评价其应急处置能力.      

面向车道变换的路径规划及模型预测轨迹跟踪

为解决智能车辆在车道变换过程中的路径规划和路径跟踪问题,首先,利用梯形加速度法设计了车道变换虚拟理想轨迹,该路径规划方法的适应性取决于车道变换时间、横向加速度及变化率等关键变量的约束条件,因而对各关键变量之间的数学关系进行了定量计算,并绘制了不同工况下的车道变换虚拟理想轨迹,用于分析各关键变量对路径规划的影响;其次,建立了线性离散的车辆动力学预测模型,综合分析了车辆模型的控制输入、状态变量以及道路结构参数等约束条件,构建了多约束模型预测控制（MMPC）系统用于车道变换路径跟踪,并基于Hildreth二次规划算法对其目标函数进行了求解,获得前轮转向角控制量,从而保证智能车辆在车道变换过程中的路径跟踪性能及操纵稳定性能;最后,利用MATLAB和Carsim软件对提出的多约束模型预测控制系统进行联合仿真,并构建单约束模型预测控制（SMPC）系统与其进行性能比较,分别对车道变换时间为3 s和6 s时的车道变换性能进行比较分析。结果表明：当车道变换时间为6 s时,2种控制系统都能较好地实现车道变换功能;当车道变换时间为3 s时,与SMPC控制系统相比较,MMPC控制系统能够在有效跟踪期望行驶路径的同时改善车辆的操纵稳定性,从而提高车辆在路径跟踪过程中的主动安全性能。     

电喷天然气发动机空燃比闭环控制及仿真研究

建立了用于控制仿真分析的天然气发动机动态模型 ;利用所建立的动态模型 ,进行了天然气发动机空燃比控制的动态仿真 ,分别采用PID控制器和神经网络控制器进行控制 ,并对控制效果进行了研究 ,比较分析了不同的控制策略在空燃比控制系统中的作用。     

Active front wheel steering model and controller for integrated dynamics control systems

We report a model and controller for an active front-wheel steering (AFS) system. Two integrated dynamics control (IDC) systems are designed to investigate the performance of the AFS system when integrated with braking and steering systems. An 8-degrees-of-freedom vehicle model was employed to test the controllers. The controllers were inspected and compared under different driving and road conditions, with and without braking input, and with and without steering input. The results show that the AFS system performs kinematic steering assistance function and kinematic stabilisation function very well. Three controllers allowed the yaw rate to accurately follow a reference yaw rate, improving the lateral stability. The two IDC systems improved the lateral stability and vehicle control and were effective in reducing the sideslip angle.     

基于加权灰色关联度的空中交通管制员工作负荷影响因素分析

定量分析影响管制员工作负荷的因素对于实现管制员工作负荷的精确评价具有重要意义。综合考虑扇区交通运行情况以及管制员工作表现,确定了14个影响管制员工作负荷的因素指标。利用灰色关联熵确定指标权重,建立了基于加权灰色关联度的管制员工作负荷影响因素分析方法,并对分辨系数的取值进行了优化。用北京01/19区域管制扇区的雷达模拟机数据对上述方法进行验证。结果表明,陆空通话次数、设备操作次数、扇区进出指数分别是影响管制员工作负荷最重要的3个因素,其加权灰色关联度分别为0.8440,0.7961,0.6935。同时,加权灰色关联度的排序结果与层次分析法得出的管制员主观排序结果基本一致。采用加权灰色关联度对管制员工作负荷影响因素进行分析能够揭示各影响因素的重要程度。      

管制员工作负荷回归模型及应用

根据空中交通管制员工作的特点,以时间为度量量化了管制员工作负荷,并分析了进离场飞行架次对于管制员工作负荷的影响。以进离场飞行架次为自变量,管制员工作时间为因变量,首次采用神经网络建立管制员工作负荷非线性回归模型;针对BP算法的缺点,提出了改进的BP算法;最后应用回归模型,结合DORATASK方法实现了对昆明终端区空域的容量评估。     

Advance-F automatic car-following model and its traffic characteristics

This study develops a longitudinal control model for automatic car-following with one switch and two controllers. The switch is operated using fuzzy logic to activate acceleration or trigger braking. The two controllers activate the acceleration pedal and the brake system and use reference adaptive control theory. A simulation is utilized to evaluate the acceptability of the proposed model and to compare the traffic characteristics between machine driving (by the proposed model) and human driving (as represented by the GM-V model). The simulation results indicate that this model is acceptable and is both safe and efficient for use in traffic. However, the automatic car-following system is more aggressive than humans are, which may be the primary challenge in implementation.     

Wheel slip control with torque blending using linear and nonlinear model predictive control

Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.     

Improved Vehicle Performance Using Combined Suspension and Braking Forces

This work presents a preliminary investigation into the integration of particular subsystems of an automobile's chassis. The specific focus of this research is the integration of Active Suspension components with Anti-Lock braking (ABS) mechanisms. The performance objective for the integrated approach is defined as a reduction in braking distance over just anti-lock brakes. Several models, of varying degrees of complexity, are presented to determine the effect of modeling accuracy on the potential performance improvement. In the most detailed model, a four degree of freedom Half Car vehicle model is developed along with models for a hydraulic Active Suspension and an ABS system. For both subsystems, actuator dynamics are included. The tire-road interface is modeled using the Magic Formula tire model. Individual controllers are developed for the subsystems and a governing algorithm is constructed to coordinate the two controllers. Simulations of the integrated controller and an ABS system, for each system model, demonstrate a significant increase in performance.     

A real-time multi-vehicle simulator for longitudinal controller design

This paper presents a new multi-vehicle simulator for platoon simulation. The main new feature of the developed simulator is a network structure for the real-time simulation of multiple vehicles, each with a detailed powertrain and engine model. It has a small initial delay, which is determined by the number of connected PCs, but the actual simulation is performed and displayed in real-time after this initial and one-time delay. Several longitudinal controllers, including a PID controller with gain scheduling, an adaptive controller, and a fuzzy controller, are also implemented in the simulator. Various system parameters can be modified interactively in the simulator screen, which is very useful for simulating a platoon of heterogeneous vehicles, in which vehicles with different dynamics and different longitudinal controllers may be involved. The simulator provides an excellent tool to develop vehicle longitudinal controllers and to study platoon behaviors. The developed simulator is also effective in testing the effects of nonlinearities neglected in the controller design phase, such as actuator delays and gear shifting schedule.     

Application of a self-tuning fuzzy PI–PD controller in an active anti-roll bar system for a passenger car

A fuzzy proportional-integral-derivative (PID) controller has not been widely investigated for active anti-roll bar (AARB) application due to its unspecific mathematical analysis and the derivative kick problem. This paper briefly explains how the derivative kick problem arises due to the nature of the PID controller as well as the conventional fuzzy PID controller in association with an AARB. There are two types of controllers proposed in this paper: self-tuning fuzzy proportional-integral–proportional-derivative (STF PI–PD) and PI–PD-type fuzzy controller. Literature reveals that the PI–PD configuration can avoid the derivative kick, unlike the standard PID configuration used in fuzzy PID controllers. STF PI–PD is a new controller proposed and presented in this paper, while the PI–PD-type fuzzy controller was developed by other researchers for robotics and automation applications. Some modifications were made on these controllers in order to make them work with an AARB system. The performances of these controllers were evaluated through a series of handling tests using a full car model simulated in MATLAB Simulink. The simulation results were compared with the performance of a passive anti-roll bar and the conventional fuzzy PID controller in order to show improvements and practicality of the proposed controllers. Roll angle signal was used as input for all the controllers. It is found that the STF PI–PD controller is able to suppress the derivative kick problem but could not reduce the roll motion as much as the conventional fuzzy PID would. However, the PI–PD-type fuzzy controller outperforms the rest by improving ride and handling of a simulated passenger car significantly.     

HEV控制器硬件在环仿真平台的研究与开发

针对控制器传统开发方法中存在的局限性以及混合动力汽车动力传动系统控制的复杂性,应用控制系统现代开发技术,为某型混合动力客车多能源动力总成控制器开发了硬件在环仿真测试平台,该平台包括实时硬件和系统模型、信号调理电路等,并利用它对控制器进行了仿真测试。仿真测试结果与试验结果说明,所开发平台模型的精度基本能够满足仿真测试要求。控制器的环境试验和在EMC试验中的成功应用以及控制器在车上的正常运行,验证了在混合动力汽车多能源动力总成控制器的开发过程中采用自行开发硬件在环仿真测试平台这一技术方案的可行性。