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.     

Control of transient flow in irrigation canals using Lyapunov fuzzy filter‐based Gaussian regulator

An optimal fuzzy filter was applied to solve the state estimation problem of the controlled irrigation canals. Using linearized finite‐difference model of the open‐channel flow, a canal operation problem was formulated as an optimal control problem and an algorithm for gate openings in the presence of unknown external disturbances was derived. A fuzzy filter was designed to estimate the state variables at the intermediate nodes based upon measured values of depth at the points in the canal. A Lyapunov function was utilized as a performance index to formulate the fuzzy interference rules of the optimal fuzzy filter. A linear quadratic Gaussian (LQG) optimal controller for a multi‐pool irrigation canal was considered as an example. The state estimation problem in the controller was simulated using two techniques: Kalman estimator and the proposed fuzzy filter. The performance of the fuzzy state estimator designed using the Lyapunov fuzzy technique was compared with the results obtained using the Kalman estimator technique. The obvious advantages of the fuzzy filter were the lower computational costs and ease of implementation. The results of this study demonstrated that proposed Lyapunov‐type fuzzy filter provides both good stability and simplicity in the control of irrigation canals more than a Kalman filter. Copyright © 2005 John Wiley & Sons, Ltd.     

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.      

基于低成本组合导航定位系统的新融合滤波算法

设计了一种可用于地面交通工具或机器人的低成本组合导航定位系统，提出了基于该系统的新信息融合方法，将模糊卡尔曼滤波算法和地图匹配技术联合起来。仿真结果表明：模糊卡尔曼滤波算法相当于一数据平滑处理窗口，具有比常规卡尔曼滤波算法更高的精度。     

结构振动瞬时最优控制的一种时滞补偿算法

 应用最优控制原理，对结构动力响应实时在线控制进行了时滞 补偿. 提出一种基于系统差分方程的瞬时最优控制算法，推导出闭环 状态反馈控制补偿增益矩阵，并讨论了补偿前后系统的稳定性. 仿真 结果表明，此算法有效地补偿了时滞带给结构动力响应的不良影响， 并保证了结构的稳定性.     

一种自适应联合卡尔曼滤波器及其在车载GPS/DR组合导航系统中的应用研究

—本文设计了实现车载ＧＰＳ／ＤＲ组合导航系统最优综合的联合卡尔曼滤波器，并给出了滤波算法。提出了一种自适应联合卡尔曼滤波器结构及其算法，并应用于ＧＰＳ／ＤＲ组合导航系统的最优综合校正中。理论分析及计算机仿真结果均表明，应用该自适应联合卡尔曼滤波器可大大提高车载ＧＰＳ／ＤＲ组合导航系统的定位精度及容错能力。     

Self-tuning fuzzy logic control of crane structures against earthquake induced vibration

Self-tuning fuzzy logic controllers (STFLC) for the active control of Marmara Kocaeli Earthquake excited crane structures are studied in this paper. Vibration control using intelligent controllers, such as fuzzy logic has attracted the attention of structural control engineers during the last few years, because fuzzy logic can handle, uncertainties and heuristic knowledge and even non-linearities effectively and easily. The improved seismic control performance can be achieved by converting a simply designed static gain into a real time variable dynamic gain through a self-tuning mechanism. A self-tuning fuzzy logic controller is designed to reduce the vibrations of the crane structure. The simulated system has a five degrees-of-freedom and modeled system was simulated against the ground motion of the Marmara Kocaeli Earthquake (M _w =7.4) in Turkey on August 17, 1999. At the end of the study, the time history of the crane bridge and portal legs displacements, accelerations, and frequency responses of the both uncontrolled and controlled cases are presented. Additionally, the performance of the designed STFLC is also compared with a PD controller. Simulations of an earthquake excited bridge and portal legs are performed to prove the validity of proposed control strategy.     

密集模态挠性结构模型多变量频域辨识和控制

密集模态挠性结构的模态不仅阻尼小而且耦合程度高,给模型辨识带来了很大困难.频域辨识是获取空间挠性结构模型的一个有效方法,但是目前频域辨识要么模型结构定义有缺陷,要么是集中在单变量情形,不适合作大型空间挠性结构多变量模型的辨识.本文提出了多变量极大似然频域辨识,给出了其模型结构定义、算法推导及实现,并将其应用于H型密集模态挠性板的模型辨识,根据辨识结果,设计主动控制律,实现了对密集模态挠性板振动的有效抑制,表明了辨识算法及主动控制的可行性.     

Self-tuning fuzzy logic control of a non-linear structural system with ATMD against earthquake

Self-tuning fuzzy logic controllers (STFLC) for the active control of Marmara Kocaeli earthquake excited building structures are studied in this paper. Vibration control using intelligent controllers, such as fuzzy logic has attracted the attention of structural control engineers during the last few years, because fuzzy logic can handle nonlinearities, uncertainties, and heuristic knowledge effectively and easily. The improved seismic control performance can be achieved by converting a simply designed static gain into a real time variable dynamic gain through a self-tuning mechanism. Self-tuning fuzzy logic controller is designed to reduce the story-drift of each floor. The simulated system has a nine-degree-of-freedom, which is modeled using nonlinear behavior of the base-structure interaction. Modeled system was simulated against the ground motion of the Marmara Kocaeli earthquake (M _w=7.4) in Turkey on 17 August, 1999. At the end of the study, the time history of the story displacements, accelerations, ATMD displacements, control voltage, and frequency responses of the both uncontrolled and controlled cases are presented. The robustness of the controller has been checked through the uncertainty in stiffness of the structure. Performance of the designed STFLC has been demonstrated for the different disturbance using ground motion of the Kobe earthquake. Simulations of an earthquake excited nine story structure are performed to prove the validity of proposed control strategy.     

结构振动的滑模变结构半主动控制

研究应用磁流变阻尼器(MRD)对结构振动半主动控制的算法和原理。研制并对磁流变阻尼器进行了阻尼特性实验，采用非线性滞回双粘性模型描述磁流变阻尼器的阻尼特性，模型结果与实验结果非常一致。采用滑模控制算法和趋近律方法设计了半主动控制器。利用滑模控制方法所建立的控制器，本文给出了地震激励下结构振动半主动控制算例。计算分析表明，半主动滑模控制具有控制效果明显、鲁棒性好等优点，是一种非常有发展前途的控制方法。     

基于广义卡尔曼滤波的桥梁结构物理参数识别

基于广义卡尔曼滤波提出了随机荷载作用下桥梁结构物理参数的识别方法。首先,以荷载为观测对象,推导出基于有限元模型的桥梁结构系统的观测方程,以结构待识别的物理参数为状态向量,建立系统状态方程;然后,对该状态方程和观测方程构成的非线性参数系统应用广义卡尔曼滤波,从而识别出结构的物理参数。对一座简支梁桥和一座三跨连续梁桥在不同工况下的物理参数识别进行了数值仿真,结果表明本文方法能够准确地识别桥梁结构全部刚度参数、质量参数和阻尼参数,且具有很强的抗噪性能,从而验证了本文方法的有效性和鲁棒性,可应用于识别大型桥梁结构的物理参数。     

Topological entropy and geometric entropy and their application to the horizontal visibility graph for financial time series

Seismic wave control is very important both in civil and mechanical engineering. Common passive methods for isolating a building or a device include base isolators and tuned mass dampers. In the present paper, a time-varying controllable spring is considered as a vibration isolator for a linear mechanical system. The controller works as follows: When the seismic movement is active, the velocity of the moving mass is monitored as the reference velocity. When such reference velocity is positive, the stiffness is reduced; when it is negative, the stiffness is increased. Numerical investigations show that the controller is capable of filtering seismic excitation close to the natural frequency of the controlled system and reducing the total seismic energy transfer up to 5 times. The role played by the gravity in the active vibration filtering is pointed out by showing that no filtering action can be observed in gravity-free simulations. Moreover, control effectiveness has been proven for a measured seismic signal, showing its robustness in presence of noise.     

Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller

This paper discusses the design of fuzzy PID type controllers (FPIDC) to improve seismic control performance of a nonlinear structural system with an active tuned mass damper (ATMD) against earthquakes. Since structural systems have nonlinearities and uncertainties, fuzzy-based controllers are adequate because of their robust character and satisfactory performance in active structural control. The main advantages of this controller are the ability to handle nonlinearities and uncertainties effectively. In the literature, various structures for fuzzy PID (including PI and PD) controllers have been proposed. In order to obtain proportional, integral and derivative control actions altogether, it is intuitive and convenient to combine PI and PD actions to form a fuzzy PID controller. The simulated system has fifteen degrees of freedom and is modeled using nonlinear behavior of the base–structure interaction. The system is then simulated against the ground motion of the Northridge earthquake (M _w =6.7) in USA on 17 January, 1994. Finally, the time history of the storey displacements, accelerations, ATMD displacements, control voltage and frequency responses of both the uncontrolled and controlled cases are presented. The ground motion recorded of the El-Centro and Kocaeli earthquakes has been used to evaluate the effectiveness of the proposed control algorithm. The robustness of the controller has been checked through the uncertainty in stiffness of the structure. Simulation results exhibit that superior vibration suppression is achieved by the use of designed fuzzy PID type controllers.     

基于级联FIR滤波器的系泊精对准算法

舰船的系泊环境存在风浪和浪涌,这些因素引起捷联惯导系统对准过程中的不确定性干扰加速度,提出一种基于级联FIR低通数字滤波器的惯性系Kalman滤波精对准算法。高频随机干扰加速度在通过级联FIR低通数字滤波器后被滤除,近似周期性干扰通过积分运算,正负部分基本抵消,因此该算法可以有效抑制系泊环境下的不确定性干扰。江上系泊对准结果为:水平精度0.02°,航向精度0.06°。试验结果表明该方案能有效完成精对准,对准精度达到中等导航精度要求。     

压电智能结构模糊自学习控制(FSLC)

将模糊逻辑与学习控制的基本思想相结合,根据控制系统的动态输出特性,采用模糊控制对学习控制律中的参数进行实时校正,实现系统的动态学习过程,提出了一种适用于压电智能结构振动控制的模糊自学控制方法FSLC（FuzzySelf-LearningContr01）。分别采用三维8节点实体单元（Solid45）和耦合单元模拟主结构和压电致动器／传感器,基于ANSYS参数化语言编写了压电智能结构振动控制分析的有限元程序。通过数值仿真证明了模糊自学习控制方法能有效控制压电结构的振动,并提高了自学习控制的收敛速度和获得了很好的控制效果。     

基于状态观测器的带分布参数体系地震响应控制

针对带分布参数体系地震响应控制问题,建立带分布参数体系的运动控制方程,推导出应用于带分布参数体系的序列最优模态控制算法。根据序列最优模态控制算法振动控制时对结构全状态反馈的需要,提出了针对序列最优模态控制算法的全维状态观测器,形成基于全维状态观测器的序列最优模态控制算法,然后通过测量容易测量的结构地震响应,由全维状态观...     

组合导航中模糊自适应Kalman滤波算法分析

以SINSiGPS组合导航系统为背景，在对Kalman滤波原理和工程应用进行深入分析的基础上，总结了该方法的不足，提出了应用神经网络和模糊推理技术对系统噪声、观测噪声和其相关阵进行直接调控的方法。该方法根据新息和新息方差的变化，实时调整自适应因子，间接改变Kalman滤波器的当前观测量和过去信息的比例关系。仿真结果表明，该算法对模型和噪声干扰有较强的自适应性，能够有效抑制滤波发散，在不损失原有精度的前提下，提高了系统的鲁棒性。     

中心刚体-柔性梁系统的最优跟踪控制

对考虑阻尼影响的中心刚体-柔性梁系统的动力特性和主动控制进行研究. 研究 中考虑了3种动力学模型：一次近似耦合模型、一次近似简化模型和线性化模型. 一次近 似模型中同时考虑了柔性梁的轴向变形和横向变形. 若在一次近似耦合模型中忽略轴向变 形的影响,则可得出一次近似简化模型. 线性化模型是对一次近似简化模型的线性化处理. 另外研究中考虑了3种阻尼因素：结构阻尼、风阻、中心刚体轴承处的阻尼. 控制设计采 用最优跟踪控制方法. 给出了从物理测量中提取模态坐标的滤波器方法. 研究结果显 示,一次近似简化模型能够有效地对系统的动力学行为进行描述;阻尼对系统的动力学特 性有着重要影响;当系统大范围运动为低速时,模态滤波器能够较好地提取出控制律所需 的模态坐标,最优跟踪控制方法能够使得系统跟踪所期望的运动轨迹,并且柔性梁的弹性 振动可得到抑制.     

基于LQG最优控制法的压电智能结构独立模态空间控制

采用压电材料作为传感器和驱动器对智能结构振动主动控制进行研究,基于机电耦合的压电智能结构传感和驱动方程,将振动控制动力学方程变换到模态空间对方程进行解耦。通过计算结构最大应变,确定压电元件的最佳粘贴位置。考虑到系统过程噪声和量测噪声的影响,设计Kalman滤波器,采用基于线性二次型高斯(LQG)最优控制的独立模态空间控制方法对压电智能结构的振动进行控制。最后以压电智能悬臂梁为例进行控制仿真,验证了此方法的有效性。     

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。