基于可拓决策法的车辆自适应避撞控制方法研究

车辆的避撞控制可以有效避免或缓解车辆的碰撞事故,是自动驾驶汽车的关键控制技术之一.各种交通条件、不确定的道路附着系数以及复杂的液压制动执行系统都会降低避撞控制的有效性.因此,本文提出了一种基于可拓决策法的自适应避撞控制,该控制方法对路面附着系数具有自适应性,并能够精确控制制动系统的制动液压力.首先,设计了滑模观测器来估计轮胎纵向力,并基于观测得到的轮胎纵向力,进一步提出带遗忘因子的递推最小二乘法估计道路附着系数.其次,基于递推最小二乘法的估计值,提出了基于路面附着系数自适应调节的自适应避撞控制方法,该方法基于可拓决策法的先决判定决定当前时刻应采用何种避撞控制策略,即采用可拓决策方法判断进行点刹预警制动、全制动或者不制动.再次,通过对执行系统-电控液压制动系统进行精确的液压控制,实现主动减速控制效果.最后,采用软件联合仿真手段对上述方法进行了验证,结果表明所提出的算法在避撞行驶工况中具有良好的避撞效果.     

基于DRBF-EKF算法的车辆质心侧偏角与路面附着系数动态联合估计

车辆质心侧偏角和路面附着系数是实现车辆底盘智能化所需要的关键参数. 车辆质心侧偏角对于提高车辆安全性和操控性至关重要, 轮胎-路面附着系数决定轮胎力的峰值, 进而确定汽车的动力学稳定性边界. 本文针对四轮独立驱动电动汽车提出了一种基于惯性测量单元、轮毂电机内置转速/转角传感器的车辆质心侧偏角和路面附着系数动态联合估计方法. 对四轮独立驱动电动汽车进行车辆动力学分析, 结合Dugoff轮胎计算模型得到车辆质心侧偏角估计器; 利用机器学习中高维数据降维PCA多元分析方法, 提取主元特征参数, 建立路面附着系数估计器. 采用可自适应调节网络结构的双径向基神经网络和扩展卡尔曼滤波DRBF-EKF方法, 通过K-means算法改进RBF神经网络结构, 扩展卡尔曼滤波进行噪声滤波提高估计精度, 实现车辆质心侧偏角和路面附着系数的动态联合估计. 通过仿真和实车实验表明, 所设计的DRBF-EKF动态联合估计器实时性和估计精度均优于扩展卡尔曼滤波算法, 可以适应车辆行驶过程中路面附着特性与车速的变化, 表现出较强的鲁棒性; 与DRBF方法相比, 显著提高了估计精度; 并且分析了可以同时满足估计精度和实时性要求的最佳隐含层神经元个数.      

基于平方根无迹卡尔曼滤波平滑算法的水下纯方位目标跟踪（英文）

为了避免被动跟踪中非线性带来的计算复杂化及跟踪精度的下降,提出将平方根无迹卡尔曼滤波平滑算法(SR-UKFS)应用到水下纯方位目标跟踪。SR-UKFS利用Rauch-Tung-Striebel(RTS)平滑算法将平方根无迹卡尔曼滤波(SR-UKF)作为前向滤波算法得到的目标状态估计向后平滑,得到前一时刻目标状态估计,再利用该状态估计值进行再次滤波得到当前时刻目标状态估计。该算法得到的前一时刻的目标状态估计更加精确,从而进一步提高了目标跟踪的精度。最后,通过对SR-UKFS算法和SR-UKF算法的跟踪性能进行了对比分析和验证,仿真结果表明在相同条件下,SR-UKFS算法能减少59%的位置误差和54%的速度误差,SR-UKFS算法应用于水下纯方位目标跟踪系统是有效的,为水下纯方位目标跟踪系统的工程实现提供了非常有价值的参考。     

基于ED-LSTM的智能汽车神经网络横向动力学建模与控制

车辆动力学建模过程中通常会进行简化和假设, 导致模型在某些工况下无法准确反映车辆的实际动态特性, 影响控制精度甚至安全性. 鉴于此, 该文提出了一种基于数据驱动的非线性建模与控制方法, 建立了新型神经网络车辆横向动力学多步预测模型, 实现了智能汽车对参考轨迹的跟踪控制. 首先, 在分析车辆单轨模型并考虑轮胎非线性和纵向负载转移的基础上, 基于编码器?解码器结构设计神经网络横向动力学模型. 其中, 使用串行排列来扩展微分方程描述不完全的动力学信息, 隐藏层神经元学习车辆的高度非线性和强耦合特性, 进而提高模型全局计算精度. 利用所构建的数据集进行模型训练和测试, 结果表明, 相比于物理模型, 所提出的模型在不同路面附着系数条件下均具有更高的建模精度, 具有隐式预测路面摩擦条件能力. 其次, 利用提出的模型设计轨迹跟踪控制算法, 根据车辆稳态转向假设, 计算所需的前轮转向角和稳态质心侧偏角, 将稳态质心侧偏角纳入基于路径误差的转向反馈中, 实现参考轨迹跟踪控制. 最后, 使用CarSim/Simulink联合仿真及HIL实验测试进行不同工况试验的对比分析, 对所提出的基于神经网络模型的控制算法进行评价, 结果表明, 该模型能够实现智能汽车在高速下精确的跟踪控制效果, 并具有良好的横向稳定性.      

基于INS/GPS组合导航的自适应模糊卡尔曼滤波

针对组合导航系统量测噪声统计特性随实际工作条件的不同而变化的特点,提出了一种基于模糊自适应卡尔曼滤波的INS/GPS组合导航算法。该方法通过监视理论残差和实际残差的协方差一致程度,应用模糊系统不断调整滤波器的增益系数,对卡尔曼滤波器进行在线自适应控制,最终实现最优估计。通过对INS/GPS组合导航系统的跑车试验,结果表明该方法在高噪声环境中具有良好的信息融合能力,能有效跟踪研究对象的状态变化。进一步表明该方法是有效、实用的。     

基于扩展H_∞滤波的单站无源目标跟踪

针对无源定位与跟踪系统可观测性弱,目标初始状态估计精度低的特点,提出了一种基于扩展H∞滤波的单站无源目标跟踪方法。将扩展H∞滤波算法对被动声纳平台获得的目标方位数据进行目标运动分析,以实现较高精度的水下仅测角目标定位和跟踪,并采用雅克比矩阵来近似处理系统的非线性测量方程。扩展H∞滤波对噪声的不确定性具有鲁棒性,保证滤波算法的数值稳定性,提高跟踪的精度和可靠性。理论分析与仿真结果表明,扩展H∞滤波跟踪速度快,性能稳定,估计精度明显优于扩展卡尔曼滤波(EKF),用于水下运动目标跟踪是可行的。     

一种基于自适应去偏转换滤波的机动目标跟踪

针对于机动目标的跟踪问题,提出了一种基于交互式多模型的自适应去偏转换卡尔曼滤波器.该算法利用交互多模型算法来完成不同跟踪模型的相互切换;根据自适应去偏转换测量卡尔曼滤波算法来推导跟踪目标状态,同时自适应因子可以确保不正常测量时的鲁棒性.与传统的去偏转换卡尔曼滤波算法对比,该算法可以很好地改善所获量测信息在雷达被干扰时的目标跟踪精度.仿真结果表明了算法的有效性和可行性,且跟踪精度相对传统的去偏转换卡尔曼滤波算法减少9.38％的位置误差.     

基于改进交互多模型概率数据关联的机动目标跟踪（英文）

为了提高杂波条件下的空中机动目标跟踪精度,提出了一个改进的交互多模型概率数据关联算法。该算法将交互多模型、去偏转换测量和概率数据关联算法相结合,利用交互多模型算法模型集合间不同模型的相互切换来估计跟踪目标的状态;利用去偏转换测量算法对转换测量误差进行去偏补偿,从而减小观测数据坐标变换引起的误差;利用概率数据关联算法处理数据关联和测量的不确定性。通过将本文的算法和基于扩展卡尔曼滤波的概率数据关联算法进行对比分析和验证,实验结果表明本文提出的算法可以提高机动目标的跟踪精度,且跟踪精度相对基于扩展卡尔曼滤波的概率数据关联算法减少26.38%的位置误差。     

强跟踪-容积卡尔曼滤波在弹道式再入目标跟踪中的应用

对于具有一定机动能力的弹道式再入目标跟踪问题,稳定性好、鲁棒性强、收敛精度高的估计方法是保证跟踪精度的关键。针对再入运动模型和测量体制的强非线性以及目标机动引起的滤波精度下降问题,提出一种将强跟踪滤波(STF)和基于三阶球面-向径容积规则的容积卡尔曼滤波(CKF)相结合的强跟踪-容积卡尔曼滤波(STCKF)。通过将强跟踪算法中的自适应渐消因子引入到滤波时间更新和测量更新方程中,在线实时调整滤波增益矩阵,能有效避免模型失准造成的滤波性能下降,使该算法兼具CKF滤波精度高和STF鲁棒性强的优点。通过数学仿真表明,改进后的STCKF可以实现对具有机动的弹道式再入目标的高精度跟踪,相对于CKF精度提高50%,并且具有更强的鲁棒性和自适应能力。     

考虑轮胎力耦合约束的智能汽车轨迹跟踪控制算法

针对在低附着变速工况下,忽略纵、侧向轮胎力耦合约束可能导致轨迹跟踪时车辆失稳问题,提出了一种模型预测控制框架下的考虑轮胎力耦合约束的车辆轨迹跟踪控制方法。首先,通过摩擦圆假设建立纵、侧向轮胎力的耦合关系,并推导与之等效的输入量边界约束,将该问题转化为约束二次规划问题;其次,提出了一种基于交叉方向乘子法的数值求解构型,降低了求解约束优化问题时Karush-Kuhn-Tucker方程的维数,实现了求解加速。仿真结果表明,在低附着变速工况下,所提出的算法能够实现最大0.166 m误差的稳定跟踪;同时数值求解过程最多仅需8次迭代,增强了控制过程的实时性。     

Analysis of torque transmitting behavior and wheel slip prevention control during regenerative braking for high speed EMU trains

The wheel-rail adhesion control for regenerative braking systems of high speed electric multiple unit trains is crucial to maintaining the stability,improving the adhesion utilization,and achieving deep energy recovery.There remain technical challenges mainly because of the nonlinear,uncertain,and varying features of wheel-rail contact conditions.This research analyzes the torque transmitting behavior during regenerative braking,and proposes a novel methodology to detect the wheel-rail adhesion stability.Then,applications to the wheel slip prevention during braking are investigated,and the optimal slip ratio control scheme is proposed,which is based on a novel optimal reference generation of the slip ratio and a robust sliding mode control.The proposed methodology achieves the optimal braking performancewithoutthewheel-railcontactinformation.Numerical simulation results for uncertain slippery rails verify the effectiveness of the proposed methodology.     

Algorithms for a Vehicle Dynamics Monitoring System,Based on Model Reference Structure

Vehicle control depends heavily on the knowledge of the vehicle operatingconditions. One of the most important parameters for its control is thetyre–road friction coefficient (µ). An appropriate way toestimate the vehicle operating conditions is the Model Reference Approach.This technique requires a model that provides estimated states which can becompared with the measured states, the difference is used to determine thereal operating conditions. This paper presents two different applications ofthe Model Reference Techniques to estimate tyre–road frictioncoefficient; these are based on the relation between tyre forces and slip,and on the vehicle lateral behaviour using an extended Kalman filter.Experimental data from the test vehicle confirms the good results obtainedin the friction estimation based on the tyre slip–force relation. Theestimation using an extended Kalman filter on lateral behaviour showsaccurate tracking. The next step to be taken is to integrate all thealgorithms in the test vehicle and to validate them for a wide range ofoperating conditions, in order to have reliable information for the activesystem control.     

基于改进卡尔曼滤波的汽车路试制动性能检测方法

针对传统汽车路试制动性能检测方法的不足,提出了一种基于改进卡尔曼滤波的汽车路试制动性能检测方法。根据卡尔曼滤波理论,以单频载波相位单点GPS接收机输出的速度和方位角作为观测量,通过改进的卡尔曼滤波递推算法高频率、高精度地推算出汽车制动过程的平面运动坐标和速度,进而确定汽车制动距离和平均减速度MFDD,以检测汽车的制动性能。实车试验表明,该方法的制动距离测量精度可达0.2~0.3 m,速度精度小于0.1 m/s,输出频率可达100 Hz,具有成本低、输出频率高、精度高、环境适应力强的优点,克服了传统方法的不足。     

Fuzzy logic adaptive Kalman filtering in the control of irrigation canals

A fuzzy logic adaptive Kalman filtering methodology was developed for the automatic control of an irrigation canal system under unknown disturbances (water withdrawals) acting in the canal. Using a linearized finite difference model of open channel flow, the canal operation problem was formulated as an optimal control problem and an algorithm for gate opening in the presence of arbitrary external disturbances (changes in flow rates) was derived. Based on the linear optimal control theory, the linear quadratic regulator (LQR), assuming all the state variables (flow depths and flow rates) were available, was designed to generate control input (optimal gate opening). As it was expensive to measure all the state variables (flow rates and flow depths) in a canal system, a fuzzy logic adaptive Kalman filter and traditional Kalman filter were designed to estimate the values for the state variables that were not measured but were needed in the feedback loop. The performances of the state estimators designed using the fuzzy logic adaptive Kalman filter methodology and the traditional Kalman filtering technique were compared with the results obtained using the LQR (target loop function). The results of the present study indicated that the performance of the fuzzy logic adaptive Kalman filter was far superior to the performance of the observer design based upon the traditional Kalman filter approach. The obvious advantages of the fuzzy logic adaptive Kalman filter were the prevention of filter divergence and ease of implementation. As the fuzzy logic adaptive Kalman filter requires smaller number of state variables for the acceptable accuracy therefore, it would need less computational effort in the control of irrigation canals. Copyright © 2009 John Wiley & Sons, Ltd.     

Stability and Bifurcation of Longitudinal Vehicle Braking

The longitudinal braking dynamics of a two-wheel vehicle model on an incline are considered using techniques from nonlinear dynamics. The model is planar and incorporates the coupled dynamics of two independently braked wheels and the vehicle body, and takes into account the slip dynamics of each wheel. By using the wheel slip values and the vehicle speed as dynamic states, it is shown that the qualitative behavior of the system can be completely captured by studying a relatively simple phase plane problem described in terms of the slip values. A systematic bifurcation analysis is carried out in which the brake torques of the two wheels are varied, and it is shown how the system transitions from stable braking to the possibility of lockup in one or both wheels, to guaranteed lockup in both wheels. In this manner a quite complete picture of the dynamic behavior is obtained as a function of the two brake torques, including regions with multiple possible steady braking outcomes, depending on the initial conditions. This analysis provides new insights into the dynamics of vehicle braking, and it provides a correction to the standard result for the critical values of the brake torques at which the wheels undergo lockup. This approach may also prove useful for evaluating brake proportioning schedules, or for investigating anti-lock braking systems and other methods of traction control.     

Comparison of the traffic performance of a two-axle four wheel drive (4WD), rear wheel drive (RWD), and front wheel drive (FWD) vehicle on loose sandy sloped terrain

The traffic performances during driving and braking of a 5.88 kN weight wheeled vehicle with two-axle four wheel drive, rear wheel drive, and front wheel drive running up and down a loose sandy sloped terrain were compared by means of a simulation. For the given dimensions of the vehicle and the given terrain-wheel system constants, the relationship between the effective tractive and braking effort of the vehicle, the amount of sinkage of the front and rear wheels, the total amount of sinkage of the vehicle, and the slip ratio were calculated to estimate the optimum height of force of application and the optimum eccentricity of the center of gravity of the vehicle. It was observed that, during driving action, the maximum effective tractive effort of the four wheel drive vehicle (4WD) was larger than that of the rear wheel drive vehicle (RWD), which in turn was greater than that of the front wheel drive vehicle (FWD). During the braking action, the effective braking effort at skid -20% of the four wheel vehicle (4WB) was larger than that of the front wheel brake vehicle (FWB), in turn greater than that of the rear wheel brake vehicle (RWB), when the two-axle four wheel vehicle is moving up or down the loose sandy sloped terrain. The maximum terrain slope angle up which the two-axle wheeled vehicle is able to move during driving action was found to be about 0.067π rad for the 4WD vehicle, about 0.031π rad for the RWD vehicle, and about 0.017π rad for the FWD vehicle. The effective braking effort at skid-20% of 4WB, FWB and RWB was found to decrease with slope angle.     

MSINS/GPS全组合系统可观测性分析

卡尔曼滤波是组合导航系统处理数据最常用的算法。首先给出PWCS可观测性定理,然后在G矩阵选星算法的基础上,设计了基于伪距/伪距率的MSINS/GPS紧耦合系统(TCS)的卡尔曼滤波器,在上述量测方程中加入基于GPS载波相位观测量的姿态测量方程,给出了有姿态测量信息的全组合系统(CIS)。最后证明了CIS满足PWCS定理条件,即可以用离散系统的选择可观测性矩阵(SOM)代替相应的连续系统的总可观测性矩阵(TOM)对系统进行可观测性和可观测度的分析。利用奇异值分解(SVD)理论给出了不同可见卫星数时CIS的可观测性分析结果。与不加入姿态观测信息的MSINS/GPS的TCS比较,计算结果表明CIS比TCS具有更好的可观测性和可观测度。