Vehicle tests of a longitudinal control law for application to stop-and-go cruise control

This paper presents the implementation and vehicle tests of a vehicle longitudinal control scheme for Stop and Go cruise control. The control scheme consists of a vehicle-to-vehicle distance control algorithm and throttle/brake control algorithm for acceleration tracking. The desired acceleration of a vehicle for vehicle-to-vehicle distance control has been designed using Linear Quadratic optimal control theory. Performance of the control algorithm has been investigated via vehicle tests. A millimeter wave radar sensor has been used for distance measurement. A stepper motor and an electronic vacuum booster have been used for throttle/ brake actuators, respectively. It has been shown that the proposed control algorithm can provide satisfactory performance.     

基于视觉的低空跟踪系统

构建了一个以无人飞行器为载体的基于视觉的低空跟踪系统。该系统由地面站和机载模块两部分组成,构建了机载自动跟踪与地面人工干预两个并联的控制回路;采用了基于灰度直方图的自适应容忍度多阈值分割算法,并在此基础上采用了基于双重子窗口的动态聚类目标提取方法;用目标的形心脱靶量作为云台的控制参数,根据目标的运动趋势对速度参数进行调整。系统通过用鼠标对监控视野中心的坐标替换目标的形心脱靶量实现机载自动跟踪和地面人工干预的平滑切换;保留不同照度下目标的灰度阈值,使得运动区域在阴影下也能被分割。经过2~3次的聚类迭代,较精确地计算出目标的形心位置,排除了干扰目标的影响,系统的处理速度达到15 frame/s。结果表明,上述算法和方法是可行的,系统具有较大的实用性。     

智能车辆巡航建模与硬件在环试验

采用软计算方法设计了智能车辆巡航的神经网络-模糊控制模型。模型采用两输入、单输出方式,两输入分别为车间距离和两车相对车速,单输出为油门量或者刹车量。为了获取模型训练和仿真的数据,设计了车辆跟随试验,试验采用GPS和蓝牙无线通信模块来实时获得两车间的相对距离和相对车速。利用试验数据对CANFIS（collective adaptive neuro-fuzzy inference system）模型进行离线训练,建立了巡航车间距离模型。根据建立的模型设计了控制器,并基于实时仿真平台DSPACE1103进行了硬件在环试验。仿真结果和试验结果对比表明,建立的模型具有一定的合理性,较好地体现了车辆系统的非线性特性。     

Low speed hybrid generalized predictive control of a gasoline-propelled car

Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem.This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.     

考虑非线性特性的电控助力制动系统多闭环压力控制策略

针对电控助力制动系统(Electro-booster brake system, Ebooster)主动制动时面临的液压系统时变特性扰动、传动机构动静态摩擦阻碍以及底层伺服电机电磁特性耦合等诸多非线性难题,提出一种考虑非线性特性的压力—位置—电流多闭环压力控制策略。分析Ebooster主动制动工作原理并建立面向控制器设计的等效简化模型;基于自抗扰理论设计压力环控制器,补偿了液压系统时变特性扰动;采用鲁棒滑模变结构设计位置环控制器,考虑传动机构动静态摩擦阻碍和系统未建模扰动;通过李雅普诺夫稳定性理论设计电流环控制器,解决永磁同步电机双轴电流耦合问题。基于d SPCAE设备搭建了硬件在环台架进行算法测试验证。结果表明,提出的多闭环压力控制策略能够控制Ebooster实现主动制动功能,压力跟随的稳态误差在0.2 MPa之内,同时在多种压力跟随工况下表现出良好的控制效果。研究成果为机-电-液耦合的线控制动系统进行制动压力控制时面临的多种非线性扰动问题,提供了一种良好的解决思路。     

重型车辆走－停巡航系统的干扰解耦控制

根据低速行驶工况下重型车辆的加/减速度响应特性,建立融合被控车辆及车间动力学特性的巡航系统非线性动力学模型。在此基础上采用非线性输出干扰解耦原理,将控制目标值（车间相对距离、速度）从系统的前导车加/减速度干扰中完全解耦出来,并实现非线性系统的线性化;基于此解耦线性化子系统,设计一种改进的走－停自适应巡航控制系统。该系统不但能够保证控制目标值取得良好的动态响应特性,而且能够实现对前导车加/减速干扰的全局解耦,可提高系统的鲁棒性,仿真计算验证了控制效果。     

汽车驾驶机器人车速跟踪神经网络控制方法

为了实现汽车驾驶机器人对给定车速的准确跟踪,提出了一种驾驶机器人车速跟踪神经网络控制方法。网络模型输入层变量为驾驶机器人油门和制动器、离合器机械腿、换挡机械手的位移;中间层为隐层,节点数为5,神经元传递函数为正切传递函数;输出层变量为试验车辆车速,神经元传递函数为线性传递函数。结果表明,该方法的收敛速度明显高于梯度下降法的收敛速度,且达到的控制精度更高,车速跟踪误差满足国家汽车试验标准的要求。     

Development of TCS slip control logic based on engine throttle control

Slip control systems are used to prevent wheel slipping and to improve acceleration performance, stability and steerability on slippery roads through the engine torque and/or brake torque control. This paper mainly deals with the engine control algorithm via adjustment of the engine throttle angle. The slip control algorithm developed in this research includes a control gain scheduling part and a road estimating part to enhance control performance. Various actual vehicle tests have been carried out on low friction roads in order to verify the developed slip control algorithm. The test results show that the controlled vehicle is superior to the non-controlled vehicle in acceleration performance and stability.     

利用自适应卡尔曼滤波实现光电跟踪中的复合控制

为了在光电跟踪控制系统中实现复合控制以提高跟踪精度,构建了基于模型自适应卡尔曼滤波算法的复合控制结构。首先,利用跟踪脱靶量数据和仪器位置数据合成目标角位置数据;然后,利用模型自适应卡尔曼滤波算法对目标角位置数据进行滤波估计以获得目标角速度信息;最后,将目标角速度信息前馈到速度回路,从而构成复合控制系统。实验结果表明:采用复合控制结构后,目标跟踪精度提高了50%。基于模型自适应卡尔曼滤波算法的复合控制技术能够在保持原反馈控制系统稳定性的条件下提高跟踪精度。     

无人驾驶越野车辆纵向速度跟踪控制试验

以无人驾驶轻型战术轮式越野车辆为平台,开展模型预测纵向速度跟踪控制实车试验研究。针对平台控制特性设计合适的下位控制器,使用Matlab/Simulink与包含气压制动系统的TruckSim车辆联合仿真初步测试系统可行性,并在沥青路和土路分别进行实车试验。试验结果表明：模型预测速度跟踪控制系统能够克服气压制动延时长、整车质量重、越野路况行驶阻力波动大等模型误差和不确定干扰,自适应调节期望加速度大小,实现不同行驶工况高精度速度跟踪。试验过程驱动/制动切换平稳、无振荡,且能够像熟练驾驶员一样充分利用发动机辅助制动,必要时既不施加电控制动,也不请求发动机输出转矩。系统使用现代车辆易于获得的车辆状态参数,便于向其他车辆移植,可作为无人车辆车体控制得力技术加以推广。     

多连杆柔性机械手末端位置的非线性预测控制

讨论了多连杆柔性机械手末端位置的控制问题。给出了一种非线性预测与刚性运动ＰＤ 反馈相结合的混合控制器,该方法可避免逆动力学反馈控制遇到的零动力学不稳定问题,稳定性分析表明,单纯非线性预测控制和ＰＤ 控制都不易保证闭环系统的稳定性,而两种方法的混合控制可保证闭环系统的稳定性。通过一个双连杆柔性机械手的仿真表明,本文提出的控制方法可实现多连杆柔性机械手末端轨迹的准确跟踪,并能消除柔性机械手的弹性震动。     

Control-oriented modeling and adaptive backstepping control for a nonminimum phase hypersonic vehicle

In this paper, the nonminimum phase problem of a flexible hypersonic vehicle is investigated. The main challenge of nonminimum phase is the prevention of dynamic inversion methods to nonlinear control design. To solve this problem, we make research on the relationship between nonminimum phase and backstepping control, finding that a stable nonlinear controller can be obtained by changing the control loop on the basis of backstepping control. By extending the control loop to cover the internal dynamics in it, the internal states are directly controlled by the inputs and simultaneously serve as virtual control for the external states, making it possible to guarantee output tracking as well as internal stability. Then, based on the extended control loop, a simplified control-oriented model is developed to enable the applicability of adaptive backstepping method. It simplifies the design process and releases some limitations caused by direct use of the no simplified control-oriented model. Next, under proper assumptions, asymptotic stability is proved for constant commands, while bounded stability is proved for varying commands. The proposed method is compared with approximate backstepping control and dynamic surface control and is shown to have superior tracking accuracy as well as robustness from the simulation results. This paper may also provide a beneficial guidance for control design of other complex systems.     

智能车辆的纵向运动控制

建立使用发动机中等复杂模型和非线性刹车模型的车辆纵向运动数学模型。使用基于模糊逻辑和遗传算法的复杂对象控制器自动设计方法设计刹车控制器,构造一种油门和刹车切换逻辑,再基于刹车控制器和切换逻辑使用自动设计方法设计油门控制器,共同构成纵向运动控制器(LMC)。仿真检验LMC在IV巡航控制和自适应巡航控制中的有效性,结果表明所设计的LMC完全可以满足IV纵向运动高精度、强鲁棒性和平顺性的要求     

An investigation on the integrated human driver model for closed-loop simulation of intelligent safety systems

This paper presents an integrated human driver model for a closed-loop simulation of the intelligent safety system. A lateral human driver model was developed to represent the steering behavior of a human driver using the finite preview optimal control method. A longitudinal human driver model represents a human driver’s throttle and brake control behavior relative to the preceding vehicle motion. It computes the desired acceleration and generates throttle/brake inputs to maintain vehicle-to-vehicle clearance at a desired level or to control vehicle speed. An integrated driver model was been developed using the longitudinal and lateral driver models to represent the behavior of a human driver in alternative driving situations, that is, vehicle following, lane following, emergency braking, and so on. Simulation studies were conducted using “Carsim” model which is validated using vehicle test data. Results showed that a human driver’s behaviors could be well represented by the integrated human driver model presented in this paper. Closed-loop simulations of a unified chassis control system with the integrated human driver model were conducted.     

Model predictive control for a class of systems with isolated nonlinearity

The paper is concerned with an overall convergent nonlinear model predictive control design for a kind of nonlinear mechatronic drive systems. The proposed nonlinear model predictive control results in the improvement of regulatory capacity for reference tracking and load disturbance rejection. The design of the nonlinear model predictive controller consists of two steps: the first step is to design a linear model predictive controller based on the linear part of the system at each sample instant, then an overall convergent nonlinear part is added to the linear model predictive controller to combine a nonlinear controller using error driven. The structure of the proposed controller is similar to that of classical PI optimal regulator but it also bears a set-point feed forward control loop, thus tracking ability and disturbance rejection are improved. The proposed method is compared with the results from recent literature, where control performance under both model match and mismatch cases are enlightened.     

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.     

Vehicle active suspension system using skyhook adaptive neuro active force control

This paper aims to highlight the practical viability of a new and novel hybrid control technique applied to a vehicle active suspension system of a quarter car model using skyhook and adaptive neuro active force control (SANAFC). The overall control system essentially comprises four feedback control loops, namely the innermost proportional-integral (PI) control loop for the force tracking of the pneumatic actuator, the intermediate skyhook and active force control (AFC) control loops for the compensation of the disturbances and the outermost proportional–integral–derivative (PID) control loop for the computation of the optimum target/commanded force. A neural network (NN) with a modified adaptive Levenberg–Marquardt learning algorithm was used to approximate the estimated mass and inverse dynamics of the pneumatic actuator in the AFC loop. A number of experiments were carried out on a physical test rig using a hardware-in-the-loop configuration that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and compared to examine the effectiveness of the system in suppressing the vibration effect on the suspension system. It was found that the simulation and experimental results were in good agreement, particularly for the sprung mass displacement and acceleration behaviours in which the proposed SANAFC scheme is found to outperform the PID and passive counterparts.     

基于ACC的制动系统模型研究

介绍了汽车自适应巡航控制系统的原理和特点,以及该系统对制动系统模型的要求;建立了汽车纵向动力学模型,并在此基础上建立了包括真空助力器、液压系统、制动器的整个制动系统的数学模型.该模型能满足控制的需要,并且较简单又具有较高的精度.对该模型进行了计算机模拟仿真,获得了较好仿真效果.     

Shifting Rule Modification Strategy of Automatic Transmission Based on Driver-vehicle-road Environment

Accidental or frequent shift often occurs when the shifting rule is built based on traditional two parameters (I.e., velocity and throttle), because the speed of engine varies slower than change of throttle opening. Currently, modifying shift point velocity value or throttle by throttle change rate is one of common methods, but the results are not so satisfactory in some working condition such as uphill. The reason is that these methods merely consider throttle change rate which is not enough for a car driving in driver-vehicle-road environment system. So a novel fuzzy control modification strategy is proposed to avoid or reduce those abnormal shift actions. It can adjust shifting rule by the change rate of throttle, current gear position and road environment information, while different gear position and driving environment get corresponding modification value. In order to compare the results of shifting actions, fuel consumption and braking distance, emergent braking in level road and extra-urban driving cycle(EUDC) working conditions with fuzzy shifting schedule modification strategy are simulated digitally. Furthermore, a hardware-in-the-loop simulation platform is introduced to verify its effect in slope road condition according to the ON/OFF numbers of solenoid valve in hydraulic system. The simulation results show that the problem of unexpected shift in those working conditions may be resolved by fuzzy modification strategy. At last, it is concluded that although there is some slight decline in power performance in uphill situation, this fuzzy modification strategy could correctly identify slope of road, decrease braking distance, improve vehicle comfort and fuel economy effectively and prolong the life of clutch system. So, this fuzzy logic shifting strategy provides important References for vehicle intelligent shifting schedule.     

基于切入工况的自适应巡航控制系统性能测试方法研究

设计了一种基于切入工况的自适应巡航系统性能测试方法,以更好地模拟实际驾驶中前车加塞的情况,并提出了可提高自适应巡航控制系统在切入工况下的驾驶体验的有效建议.介绍了自适应巡航系统系统框架、测试设备、测试场地以及测试工况;采用控制变量法,分别以两车速度差、前车切入纵向距离和前车切入横向速度为单一变量,详细制定自适应巡航系统...