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.     

Active control for seismic response reduction using modal-fuzzy approach

An active modal-fuzzy control method using hydraulic actuators is presented for seismic response reduction. In the proposed control system, a new fuzzy controller designed in the modal space produces the desired active control force. This type controller has all advantages of the fuzzy control algorithm and modal approach. Since it is very difficult to select input variables used in fuzzy controller among numerous state variables in the active fuzzy control system, the presented algorithm adopts the modal control algorithm to be able to consider information of all state variables in civil structures that are usually dominated by first few modes. In other words, all information of the whole structure can be considered in the control algorithm evaluated to reduce seismic responses and it can be efficient for civil structures especially. In addition, the presented algorithm is expected to magnify utility and performance caused by efficiency that the fuzzy algorithm can handle complex model more easily. An active modal-fuzzy control scheme is applied together with a Kalman filter and a low-pass filter to be applicable to real civil structures. A Kalman filter is considered to estimate modal states and a low-pass filter was used to eliminate spillover problem. The results of the numerical simulations for a wide amplitude range of loading conditions and for historic earthquake show that the proposed active modal-fuzzy control system can be beneficial in reducing seismic responses of civil structures.     

自适应卡尔曼滤波在惯导初始对准中的应用研究

本文研究了自适应卡尔曼滤波技术在惯导系统中的应用。在噪声统计特性未知或近似已 知的情况下,采用常规卡尔曼滤波会导致较大的状态估计误差,甚至使滤波发散;而自适应卡 尔曼滤波在估计状态的同时,利用观测数据带来的信息,可在线估计噪声的统计特性,从而不 断地改进滤波器的设计,由此得到的滤波估计比常规卡尔曼估计精度更高。本文采用Ｓａｇｅ 和 Ｈｕｓａ 自适应滤波算法,结合惯导初始对准,给出了计算机仿真。仿真结果进一步证实在噪声统 计特性不确切知道的情况下,自适应卡尔曼滤波的估计精度高于常规卡尔曼滤波的估计精度。     

基于FFUKF路面附着系数估计的汽车牵引力控制

以后驱牵引车为研究对象, 设计了基于路面附着系数估计的牵引力控制系统(TCS). 在路面附着系数估计方面, 针对传统卡尔曼滤波难以跟踪时变非线性系统的问题, 本文将模糊控制理论和衰减记忆滤波思想引入无迹卡尔曼滤波, 设计一种基于模糊遗忘因子的无迹卡尔曼滤波路面附着系数估计方法, 提高了算法的跟踪性能. 牵引力控制包括扭矩控制和制动控制. 在TCS扭矩控制方面, 分别利用路面附着系数和驱动轮滑转率在目标滑转率附近时的车辆加速度计算目标基础扭矩, 根据车辆行驶状态和抖振度参量, 基于可拓控制理论划分经典域、可拓域和非域, 通过可拓集的关联函数得到动态权重系数, 将上述两种方法计算得到的目标基础扭矩进行可拓融合设计出基础扭矩. 之后, 以实际滑转率和目标滑转率之间的误差作为输入, 采用模糊自整定PI控制器得到目标反馈扭矩. 在制动控制方面, 针对两种典型路面分别设计了PI控制压力和附着差压力. 实车试验结果表明, 基于模糊遗忘因子的无迹卡尔曼滤波算法能够更加快速地跟踪路面附着系数的变化, 同时基于路面附着系数估计的TCS控制策略能够有效抑制驱动轮过度滑转, 将驱动轮滑转率控制在最佳范围内, 显著提高了车辆的动力性.      

Robust adaptive sliding mode control of MEMS gyroscope using T–S fuzzy model

In this paper, a multi-input multi-output Takagi–Sugeno (T–S) fuzzy model is proposed to represent the nonlinear model of micro-electro mechanical systems (MEMS) gyroscope and improve the tracking and compensation performance. A robust adaptive sliding mode control with on-line identification for the upper bounds of external disturbances and an adaptive estimator for the model uncertainty parameters are proposed in the Lyapunov framework. The adaptive algorithm of model uncertainty parameters could compensate the error between the optimal T–S model and the designed T–S model, and decrease the chattering of the sliding surface. Based on Lyapunov methods, these adaptive laws can guarantee that the system is asymptotically stable. For the purpose of comparison, the designed controller is also implemented on the nonlinear model of MEMS gyroscope. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme on the T–S model and the nonlinear model.     

基于交互式多模型秩滤波的移动机器人组合导航算法

针对非结构化环境下移动机器人组合导航系统中存在的时变或非高斯噪声,将秩滤波器(rank Kalman filter,RKF)与交互式多模型算法(interactive multiple model filter,IMM)相结合,提出一种交互式多模型秩滤波算法(IMM-RKF)。秩滤波根据秩统计量相关原理确定采样点和权值,可适用于具有非高斯噪声的非线性系统;交互式多模型算法是解决结构和参数易发生变化系统中状态估计问题的重要途径,能够抑制组合导航系统中时变噪声引起的导航参数估计误差。仿真实验表明,相比于交互式多模型扩展卡尔曼滤波(IMM-EKF)和交互式多模型无迹卡尔曼滤波(IMM-UKF),提出的IMM-RKF算法能够提高组合导航系统姿态、速度和位置估计精度。     

基于UKF的INS/GNSS/CNS组合导航最优数据融合方法

为了提高INS/GNSS/CNS组合系统的导航精度,提出了一种基于UKF的多传感器最优数据融合方法。该方法具有两层融合结构,第一层中,GNSS和CNS分别通过两个局部UKF滤波器与INS组合,以并行的方式获得INS/GNSS和INS/CNS子系统的局部最优状态估值;第二层中,根据线性最小方差准则推导出一种矩阵加权数据融合算法,对局部状态估值进行融合,获取系统状态的全局最优估计。提出的方法无需采用方差上界技术对局部状态进行去相关处理,克服了联邦卡尔曼滤波(FKF)及其优化形式存在的缺陷。仿真结果表明,相比于FKF,提出方法的导航精度可至少提高36.4%;相比于UKF-FKF,其导航精度也可至少提高21.0%。     

Dead-zone effect on the performance of state estimators for hydraulic actuators

State estimation in hydraulic actuators is a fundamental technique for fault detection and it is also a valid tool useful to reduce the installation of sensors. The performance of the linear/linearization based techniques for the state estimation is strongly limited due to hard nonlinearities that characterize hydraulic actuator. One of the most common hard nonlinearities in hydraulic actuator is the dead-zone. This paper focuses on an alternative nonlinear estimation method that is able to fully take into account dead-zone hard nonlinearity and measurement noise. The estimator is based on the state-dependent-Riccati-equation (SDRE). A fifth order nonlinear model is derived and employed for the synthesis of the estimator. Several simulations have been conducted in order to analyse the effect of the dead-zone characteristic on the novel estimator performance, showing comparisons with the largely used extended Kalman filter (EKF). Numerical results demonstrate the effectiveness of SDRE based technique in applications characterized by extended dead-zone for which the EKF method provides poor results.     

基于卡尔曼滤波的信息融合算法优化研究

通过比较采用联邦卡尔曼滤波的状态向量融合和量测信息融合,得出量测信息融合优于状态向量融合,因为只有当卡尔曼滤波一致时状态向量融合才有效.采用基于最小均方差估计的观测值加权融合法融合了多传感器数据,保持了观测向量的维数.这种方法具有高效性.为了提高该算法的速度和精度,对系统的量测空间进行了等价变换,而等价系统的状态空间却没有改变.给出了等价变换前后的系统误差方差阵和状态估计均一致性的证明.把矩阵分析中的L-D分解算法运用到该算法中以避免计算矩阵的逆,从而改善了算法的稳定性和精度.举例验证了所设计算法的这些优点,给出了采用联邦卡尔曼滤波和所优化滤波算法的状态估计和误差的仿真结果,并依次进行了分析.经过这种优化,算法的精度和速度得到很大提高,已经应用到实际工程中.     

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.      

Synchronization of chaotic systems using fuzzy impulsive control

This paper investigates the problem of fuzzy impulsive control to synchronize two chaotic systems using a novel time-dependent Lyapunov function approach. Compared with the existing time-independent Lyapunov methods, the proposed method enables us to exploit more information on the impulsive intervals. Initially, using the Lyapunov technique and two parameterized linear matrix inequality (LMI) techniques, some less conservative synchronization criteria via a fuzzy impulsive controller using the states of both drive and response chaotic systems are derived. Subsequently, an LMI approach to designing such a fuzzy impulsive controller is developed to realize the synchronization. Finally, the proposed method is applied to the chaotic Lorenz system and Rösler system to illustrate its effectiveness.     

Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks

This paper proposes a new delay-dependent state estimator for Takagi–Sugeno (T-S) fuzzy delayed Hopfield neural networks. By employing a suitable Lyapunov–Krasovskii functional, a delay-dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is asymptotically stable. It is shown that the design of the proposed state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.     

一种新的惯性导航初始对准滤波方法

Unscented卡尔曼滤波(UKF)在算法实现和估计精度方面均优于传统的扩展卡尔曼滤波(EKF)。但是当系统状态的维数比较高时，非局部的采样导致估计误差变大，此时需要采用尺度变换模式的UKF(SUKF)方法。中在惯导系统静基座初始对准的非线出虑波问题中引入SUKF，并通过仿真对比了新方法和EKF的估计效果。实验表明，新方法的收敛速度和估计精度均好于EKF。     

CDKF approach for estimating a static parameter of carrier frequency offset based on nonlinear measurement equations in OFDM systems

“Central difference Kalman filtering (CDKF)” is proposed as a new state of the art approach for carrier frequency offset estimation in orthogonal frequency division multiplexing systems. The parameter of interest to be estimated in this problem is a static value rather than a dynamically varying parameter. Therefore, classical approaches (e.g., maximum likelihood method or best linear unbiased estimator) might be more pertinent than Bayesian approaches if it is assumed to be a deterministic value. Nonetheless, it is shown and justified that a recently developed extended Kalman variant, i.e., CDKF, outperforms previously proposed methods in terms of mean squared error with efficient processing speed. Particularly, it is shown that CDKF outperforms recently proposed Gaussian particle filter for this one-dimensional static parameter estimation problem.     

基于自适应UKF算法的MEMS陀螺空中在线标定技术

为保证微型卫星定位应用中系统精度与稳定性,需要对姿态传感器进行实时在线标定.在无外界姿态参考时,提出一种用三轴磁强计测量值来实时估计MEMs陀螺的零漂误差的方法,采用UKF滤波算法,将陀螺漂移作为滤波状态向量,通过建立三轴磁强计测量微分方程,作为系统量测方程实现陀螺漂移的最优估计.针对磁强计测量信息易受干扰导致滤波量测模型不准确的问题,将自适应因子引入到UKF中,通过在线监控和调整测量误差,减少陀螺标定的估计误差,增强系统性能.实验结果表明,经过标定,MEMS陀螺精度提高约30%,并且在磁强计有外界干扰时,陀螺的标定结果收敛.将标定后的MEMS陀螺进行姿态解算,其动态误差小于2°.     

捷联惯导系统初始对准中Kalman参数优化方法

针对Kalman滤波器在捷联惯导系统（SINS）初始对准中的应用，系统分析了Kalman滤波器参数（包括估计误差协方差阵初值P0，模型噪声方差阵Q和量测噪声方差阵R）选取对系统状态变量的估计精度和收敛速度的影响。采用协方差性能分析法，进行了Kalman滤波器参数优化仿真，仿真结果表明：调整扁的取值可改变状态变量估计的收敛速度，调整Q或R的取值，既可改变状态变量（尤其是陀螺误差）的收敛速度又可改变它们的估计精度。综合考虑时，局的取值要比真实值大一些，Q和R的取值要比真实值小一些，这样既可缩短陀螺误差和加速度计偏置误差的估计时间，又可提高它们的估计精度。中还给出了使滤波器正常可靠工作的P0、Q和R参数的范围。     

飞机操纵面故障诊断及其性能评估

针对操纵面故障将严重影响飞机的飞行安全,提出一种能快速实现故障诊断及性能评估的系统方法。首先,当系统状态维数较高时,采用容积卡尔曼滤波算法的球形积分准则和径向积分准则优化Sigma 点的采样策略和权重分配,较好地解决了无迹卡尔曼滤波算法滤波性能明显下降的问题;然后,利用飞机等速平飞运动特征计算故障下的升力系数和阻力系数,得出能反映飞机性能的飞行包线;最后,通过仿真结果验证了本文所提方法的可行性。     

AIRCRAFT CONTROL SURFACE FAULT DIAGNOSIS AND EVALUATION OF FLIGHT PERFORMANCE

针对操纵面故障将严重影响飞机的飞行安全,提出一种能快速实现故障诊断及性能评估的系统方法。首先,当系统状态维数较高时,采用容积卡尔曼滤波算法的球形积分准则和径向积分准则优化Sigma 点的采样策略和权重分配,较好地解决了无迹卡尔曼滤波算法滤波性能明显下降的问题;然后,利用飞机等速平飞运动特征计算故障下的升力系数和阻力系数,得出能反映飞机性能的飞行包线;最后,通过仿真结果验证了本文所提方法的可行性。     

基于模糊逻辑控制的舰船组合导航自适应卡尔曼滤波器

双天线GPS提供的载体姿态信息与惯性导航系统信息进行融合可提高组合导航系统的性能。由于在实际应用中,GPS接收机可能会受到某种干扰无法提供舰船航向信息,从而降低传统卡尔曼滤波器的性能。因而提出了一种新的基于模糊逻辑控制的自适应卡尔曼滤波器。改进后的卡尔曼滤波器使用两个模糊逻辑控制器来调整两个系统的组合模式,并且根据卡尔曼滤波器的内部状态、GPS工作状态和舰船运动状态来计算卡尔曼增益。通过使用INS和GPS的实测数据验证,这种基于模糊逻辑控制的自适应卡尔曼滤波器能有效的提高INS/GPS组合导航系统的性能。