感应电机的Takagi-Sugeno型模糊状态观测器

针对感应电机在低速发电状态时传统自适应磁链观测器性能不稳定的问题,提出了基于Takagi-Sugeno模糊模型的状态观测方法.该方法利用电机的5阶非线性时变模型进行磁链观测和速度估计.根据Lyapunov稳定理论和线性矩阵不等式技术设计了模糊观测器.仿真结果表明,将该方法用于感应电机无速度传感器控制,速度估计误差小于1.78%,感应电机在全速度范围内各种工况下都能稳定工作.     

从二阶规则到规则丛——对哈特法理论的一种描述社会学延伸

改造哈特的法理论,并建立起与法社会学共享的描述社会学一直是不少人的愿望.努力的可能方式之一,是在哈特规则体系中植入取消规则,并把原本规整的二阶规则扩展为具有社会生态关系的规则丛.虽然改造后的规则理论已不是纯粹意义上的实证主义法理论,但它没有破坏哈特规则理论原有的内部基本关系,并能够整合法社会学的既有概念资源,实现对中国法制现实的立面解释.     

A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations

A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.     

基于状态观测器的城市快速路交通事故识别

针对城市快速路网中交通事故频发的现象,为及时准确地对事故进行识别,提出一种基于宏观交通流模型的状态观测器估计算法.根据利用交通仿真软件Paramics的实验数据,并结合元胞传输模型(CTM)理论分析事故发生前后,事故路段及其上下游路段的交通流密度分布特征.同时基于路网的交通流模型构建了城市快速路事故的状态观测器估计模型,模型通过估计密度的变化规律,并结合交通状态分布特征来对事故进行识别.以京通快速路为例,通过对观测器估计误差进行计算,得出了实验路段平均百分比误差(MPE)的均值为11.56%,模型估计精度为88.44%.该方法能较为准确的对事故进行识别,为快速路中的交通事故识别提供有效的参考.     

低成本AHRS中Kalman滤波器的降维状态观测器设计

最优控制系统如捷联惯导系统（AHRS）都离不开状态反馈。然而系统的状态变量并不都是易于直接检测到的，这就需要状态观测器。基于减轻导航计算机计算负担和降低成本的考虑，提出Kalman滤波器的降维观测器的设计，并且讨论了误差模型的噪声补偿和降低估值误差的方法。     

基于全维观测器的无速度传感器异步电机控制

提出了一种模型参考自适应观测器，用于异步电机无速度传感器矢量控制系统的转速辨识。该方法将电机矢量控制与模型参考自适应系统（MRAS）相整合，异步电机作为参考模型，而全阶观测器为可调模型，利用两模型输出的误差构造了一个自适应律。理论分析和仿真结果表明，该转速辨识方法具有较强的鲁棒性和令人满意的动静态性能。     

基于滑模观测器的LSLSM位置辨识研究

基于长定子直线同步电机(LSLSM)的磁浮列车在中高速运行时,传统的车载位置传感器无法准确获取电机位置信息,并且位置信息易受到干扰,为此提出基于滑模观测器的位置辨识方法。在分析滑模观测器对定子电阻、d轴电感和q轴电感参数敏感性基础上,采用一种基于q轴电感校正的LSLSM位置辨识方法,可提高位置观测精度和系统可靠性。通过数字仿真和磁浮试验线验证了所提方法的正确性。     

用于城市轨道交通车辆永磁同步电机控制的一种新型滑模观测器

针对传统滑模观测器无法满足城市轨道交通车辆永磁同步电机无传感器控制的精度问题,使用了一种新型滑模观测器。通过建立城市轨道交通车辆永磁同步电机数学模型,在采用Sigmoid函数消除抖振的同时,引入反电动势观测器来估算反电动势值,消除了相位滞后;并采用指数趋近律代替传统等速趋近律。仿真和试验结果表明,与传统滑模观测器相比,新型滑模观测器能更好地抑制抖振,具有更好的观测精度和鲁棒性,达到了城市轨道交通车辆用永磁同步电机无传感器技术的控制要求。     

Using the lead vehicle as preview sensor in convoy vehicle active suspension control

Both ride quality and roadholding of actively suspended vehicles can be improved by sensing the road ahead of the vehicle and using this information in a preview controller. Previous applications have used look-ahead sensors mounted on the front bumper to measure terrain beneath. Such sensors are vulnerable, potentially confused by water, snow, or other soft obstacles and offer a fixed preview time. For convoy vehicle applications, this paper proposes using the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. A robust observer is used to estimate the states of a quarter-car vehicle model, from which road profile is estimated and passed on to the follower vehicle(s) to generate a preview function. The preview-active suspension, implemented in discrete time using a shift register approach to improve simulation time, reduces sprung mass acceleration and dynamic tyre deflection peaks by more than 50% and 40%, respectively. Terrain can change from one vehicle to the next if a loose obstacle is dislodged, or if the vehicle paths are sufficiently different so that one vehicle misses a discrete road event. The resulting spurious preview information can give suspension performance worse than that of a passive or conventional active system. In this paper, each vehicle can effectively estimate the road profile based on its own state trajectory. By comparing its own road estimate with the preview information, preview errors can be detected and suspension control quickly switched from preview to conventional active control to preserve performance improvements compared to passive suspensions.     

Robust road friction estimation during vehicle steering

Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.