无人机自适应姿态角度估计仿真

针对复杂环境下传感器噪声未知且不断变化,会导致姿态融合结果不准确的问题,设计了一种基于单新息自适应算法的卡尔曼滤波器,对加速度计和陀螺仪噪声协方差进行在线估计。首先,介绍了能够结合各个传感器优势的无人机姿态融合算法。然后,设计了采用基于单新息自适应算法的卡尔曼滤波器,给出了能够在线估计加速度噪声协方差R和陀螺仪噪声协方差Q的自适应算法。MATLAB仿真表明单新息自适应卡尔曼滤波器在环境噪声变化时,能够更准确地获得无人机的姿态信息,提高了姿态融合精确度,提高了滤波器的鲁棒性。     

多算法结合的汽车参数和状态估计方法研究

为实时准确获取汽车参数及状态信息以提高汽车主动安全性能,提出了一种多算法结合的自适应估计算法。该算法将递推最小二乘算法、蚁群优化算法及容积卡尔曼滤波算法进行有效结合,同时将含有不准确模型参数及未知时变噪声的三自由度非线性整车模型作为标称模型。采用递推最小二乘算法实时估计汽车参数,引入蚁群优化算法实时跟踪容积卡尔曼滤波器的过程噪声及量测噪声,根据目标函数对噪声协方差进行寻优,以解决系统的噪声时变问题,从而获取汽车状态的准确估计。基于CarSim/Simulink的仿真实验结果表明,该算法的状态估计精度高,且具备汽车模型参数校正能力,可以满足系统的控制需要。     

协方差重置的两阶段递推贝叶斯参数辨识算法

为了在有色噪声干扰情况下获得无偏估计,基于辅助模型思想和分解技术,提出了一种带协方差重置的两阶段递推贝叶斯辨识算法。该算法首先把待辨识模型分解成两个虚拟子模型,然后分别辨识;同时,把估计到的噪声方差引入算法,并加入了一种新的协方差重置方法。计算量分析表明,与带协方差重置的最小二乘算法相比,所提算法可以减少计算量。仿真结果显示,所提算法的估计误差比传统最小二乘算法要小。实例建模证明了算法的有效性。     

有色噪声干扰输出误差系统的偏差补偿递推最小二乘辨识方法

借助于偏差补偿原理和预滤波思想, 推导了有色噪声干扰输出误差系统参数估计的偏差补偿递推最小二乘 (Bias compensation recursive least squares, BCRLS) 辨识方法. 该方法降低了辨识对输入信号平稳性的要求, 实现了偏差补偿方法参数估计的递推计算, 可以用于在线辨识. 提出的递推 BCRLS 辨识方法优于非递推偏差补偿最小二乘算法, 提高了参数估计精度. 仿真试验证实了算法的有效性.     

一种高精度APNCKF算法在谐波检测中的应用

为了进一步提高含噪环境下谐波检测的精确度,提高卡尔曼滤波器的稳定性,对系统噪声协方差进行了分析,通过不断的在线辨识出过程噪声协方差,提出了一种自适应过程噪声协方差卡尔曼滤波算法。该算法利用序贯最大化可信度更新先验信息来辨识过程噪声,然后通过卡尔曼滤波器进行迭代运算,估计出相应的幅值和相位。该算法最大的特点就是辨识出的过程噪声Q的骤然增大匹配的即是谐波幅值暂降的出现。通过在MATLAB环境下进行谐波仿真验证,结果表明该算法在准稳态条件下较好地跟踪电力系统谐波状态,且与常规卡尔曼、基于最大似然准则的卡尔曼、小波/小波包变换相比,该自适应算法的收敛速度较快、滤波精度高、实时性以及稳定性较好,具有重要的工程实际意义。     

基于辅助模型的递推增广最小二乘辨识方法

针对有色噪声干扰的输出误差滑动平均系统, 将辅助模型与递推增广最小二乘算法相结合: 用辅助模型的输出代替辨识模型信息向量中的未知真实输出项, 用估计残差代替信息向量中的不可测噪声项, 从而提出了基于辅助模型的递推增广最小二乘辨识方法. 为了展示所提方法的特点, 文中还给出了经过模型变换的递推增广最小二乘算法. 理论分析和仿真研究表明, 提出的方法原理简单、计算量小, 可以给出高精度参数估计, 且能够用于在线辨识.     

联合白噪声统计估计和卡尔曼滤波的MIMO信道辨识

提出了一种新的时间选择性衰落环境下MIMO信道辨识算法。为了提高信息传输效率,训练序列被直接叠加于信息序列之上。算法将信息符号输出、接收端AWGN和由于采用零中频接收技术而产生的直流偏移当做虚拟的观测噪声,其均值和自协方差均未知。通过联合的递推白噪声统计估计器和卡尔曼滤波器对时变信道进行跟踪,推导了一种计算简单的次优无偏时变白噪声统计估计器。以简单有效的方法抑制直流偏移对辨识精度的影响。仿真结果表明了算法具有良好的性能。     

损失数据线性参数系统的递推最小二乘辨识方法

针对损失数据线性参数系统的参数辨识问题, 借助辅助模型辨识思想推导出其变递推间隔辅助模型递 推最小二乘算法.为了提高该算法的计算效率, 利用分解技术得到变递推间隔分解递推最小二乘算法 估计系统参数.此外, 在变递推间隔分解递推最小二乘算法中引入遗忘因子, 从而提高参数估计精度和收敛速度.仿真结果表明, 所提出的算法能有效估计系统参数.     

基于动态遗忘因子最小二乘与EKF的电池SOC估计

电池荷电状态SOC（State Of Charge）作为电池管理系统中尤为重要的一部分,其准确估计成为锂离子电池研究的重点。为了提高动态工况下的SOC估计精度,对锂离子电池等效模型进行分析,基于AIC（赤池信息）准则确定二阶RC电路为等效电路模型,使用递推最小二乘算法对模型参数进行在线辨识,为提高辨识精度,提出了改进带动态遗忘因子递推最小二乘算法,对算法加入遗忘因子,通过电压结果误差实时动态调整算法遗忘因子取值。将递推最小二乘算法和含动态遗忘因子最小二乘算法分别与扩展卡尔曼滤波（EKF）算法进行SOC联合估计,并对比其预测效果,结果表明含有动态遗忘因子最小二乘与EKF联合估计模型具有更高的精度和鲁棒性。     

基于偏差补偿递推最小二乘的Hammerstein-Wiener模型辨识

许多实际系统可以表示成一种中间为线性动态环节、输入输出端为非线性静态环节的Hammerstein-Wiener模型. 针对含过程噪声的Hammerstein-Wiener模型, 提出一种改进在线两阶段辨识方法. 第一步采用偏差补偿递推最小二乘法在线辨识含原系统参数乘积项的参数向量. 通过在递推最小二乘算法中引入一个修正项, 补偿过程噪声引起的估计偏差. 第二步采用基于张量积逼近的奇异值分解法分离出原系统各参数的值. 通过引入两个矩阵的张量积逼近加权最小二乘的权系数, 提高参数分离精度. 理论分析和计算机仿真验证了本文方法的有效性.     

Dual‐rate sampled‐data stabilization for active suspension system of electric vehicle

In this paper, we consider the dual‐rate sampled‐data state‐feedback control problem for an active suspension system of an electric vehicle. In the active suspension system, there exist 2 accelerometers to measure the heave acceleration of the sprung mass and the vertical acceleration of the unsprung mass, respectively. When the 2 accelerations are measured by sampled data under different sampling periods, the difficulty arising from the dual‐rate sampled data makes the active suspension stabilization problem challenging but interesting. In this paper, a linear hybrid stabilizer is proposed, which is implemented using dual‐rate sampled‐data state feedback. In order to deal with the more difficult stabilization problem under different triggering time instants, a coordinate transformation is proposed. A useful technical theorem is proposed in the stability analysis to show that the proposed hybrid controller can guarantee the states of the active suspension system being asymptotically stabilized or at least bounded to arbitrarily small domains. The experiment result is similar to the simulation result and indicates that the proposed active suspension controlling system is effective.     

考虑道路友好性的载货汽车悬架参数优化仿真

为了减少汽车轮胎动载荷对路面的损伤,研究兼顾行驶平顺性和道路友好性的悬架参数优化方法.建立了四自由度二分之一汽车振动模型,给出了整车振动方程、频响函数和路面输入模型,针对该汽车常用的行驶条件,以行驶平顺性指标和道路友好性指标的线性加权和作为优化目标,运用Matlab优化工具,对B级和C级路面典型工况的悬架刚度和阻尼进行仿真优化.优化结果表明,悬架参数对行驶平顺性和道路友好性有较大的影响;与原车相比,悬架参数优化后,行驶平顺性提高了5%～15%,道路友好性提高了2%～3%;针对B级和C级路面条件得到的两组优化结果为悬架参数没计提供了可行的目标范围.     

Robust active suspension design subject to vehicle inertial parameter variations

This paper presents an approach in designing a robust controller for vehicle suspensions considering changes in vehicle inertial properties. A four-degree-of-freedom half-car model with active suspension is studied in this paper, and three main performance requirements are considered. Among these requirements, the ride comfort performance is optimized by minimizing the H∞ norm of the transfer function from the road disturbance to the sprung mass acceleration, while the road holding performance and the suspension deflection limitation are guaranteed by constraining the generalized H2 (GH2) norms of the transfer functions from the road disturbance to the dynamic tyre load and the suspension deflection to be less than their hard limits, respectively. At the same time, the controller saturation problem is considered by constraining its peak response output to be less than a given limit using the GH2 norm as well. By solving the finite number of linear matrix inequalities (LMIs) with the minimization optimization procedure, the controller gains, which are dependent on the time-varying inertial parameters, can be obtained. Numerical simulations on both frequency and bump responses show that the designed parameter-dependent controller can achieve better active suspension performance compared with the passive suspension in spite of the variations of inertial parameters.     

基于磁流变阻尼器整车半主动悬架的开关控制

利用一般系统的第2类Lagrange方程建立了适合磁流变阻尼器半主动悬架的Lagrange方程,并在此基础上建立了侧倾、俯仰和垂直运动完全耦合的整车半主动悬架系统运动方程和状态方程．以某种磁流变阻尼器作为作动器,系统地研究了整车半主动悬架开关控制的策略．仿真结果表明：开关控制对整车悬架的簧载质量的垂直加速度和侧倾角加速度的控制效果不明显,特别是对俯仰角加速度反而有所恶化．但是,对悬架动挠度和轮胎动挠度,和非簧载质量的垂直加速度,簧载质量的侧倾加速度可以进行有效的控制,特别是对后悬架的控制效果尤其显著．     

Adaptive fuzzy controller with sliding surface for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate model for designing a model-based controller. Here, an adaptive fuzzy sliding mode controller is proposed to suppress the sprung mass position oscillation due to road surface variation. This intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms. It has online learning ability to deal with the system time-varying and nonlinear uncertainty behaviors, and adjust the control rules parameters. Only eleven fuzzy rules are required for this active suspension system and these fuzzy control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control algorithm effectively suppresses the oscillation amplitude of the sprung mass with respect to various road surface disturbances.     

Optimal ride height and pitch control for championship race cars

Car performance in championship auto racing is strongly influenced by aerodynamics, particularly ground effects aerodynamics. Vehicles employing this technology require careful control of ride height and unsprung mass pitch in order to be stable, perform properly and achieve maximum success on the racetrack. In the past, attempts for controlling these vehicle variables centred around employment of very stiff “suspension systems” so as to prevent aerodynamic load buildup, which occurs at high race velocities, from causing excessive suspension travel. Control of these variables, however, may require use of an active suspension system, coupled with a controller which can adjust the suspension system in such a way as to mask road surface disturbance and aerodynamic noise. In the present work, a controller is developed for such a vehicle/suspension combination using linear quadratic stochastic regulator theory. Vehicle road inputs are modelled as Gaussian white noise, and are nearly completely rejected by the controller. In addition, representative aerodynamic inputs are also well controlled so that vehicle outputs of unsprung mass pitch angle and centre of gravity height are held constant.     

Estimation of vehicle sideslip, tire force and wheel cornering stiffness

This paper presents a process for the estimation of tire–road forces, vehicle sideslip angle and wheel cornering stiffness. The method uses measurements (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities) only from sensors which can be integrated or have already been integrated in modern cars. The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire–road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. The vehicle mass is assumed to be known. Experimental results show the accuracy and potential of the estimation process.     

Passenger body vibration control in active quarter car model using ANFIS based super twisting sliding mode controller

This paper presents passenger body vibration control using an Adaptive Neuro Fuzzy Inference System (ANFIS) based super twisting sliding mode controller (ASTSMC) in active quarter car system. The proposed quarter car model is having three degrees of freedom composed of passenger body, sprung mass and unsprung mass. The random road profile is generated using ISO 8608 standard. The ride comfort of passenger body is calculated as per ISO 2631-1 standard. The simulation response is studied in time and frequency domain for passenger body acceleration and displacement in quarter car model. The response generated by ASTSMC controller for passenger body vibration suppression is compared with super twisting sliding mode controller and passive suspension system. The graphical and mathematical results proved the superiority of proposed ASTSMC controller in providing best ride comfort and safety to travelling passenger.     

基于电磁作动器的车辆座椅悬架最优控制研究

为了改善农用车、工程车等车辆座椅的减振性能,以电磁作动器为执行器,建立人体座椅—车辆两自由度的主动座椅悬架系统模型。通过对该系统的动力学模型进行线性化处理,并应用二次型最优控制理论,选取合适的加权系数,实现系统的最优控制。在Matlab/Simulink中以白噪声路面激励为系统输入,对主动控制和被动控制的座椅悬架系统仿真分析。结果表明：在不同的激励条件下,基于电磁作动器的主动座椅悬架系统减振效果显著,大幅降低了驾驶员所承受垂直振动加速度,提高了车辆的乘坐舒适性和操纵稳定性。