The adaptive Kalman filter based on fuzzy logic for inertial motion capture system

In the inertial motion capture system, the model complexity and the large amount of computation make the completion of the orientation estimation algorithm rely solely on PC. Because the data processing speed is slow, it is difficult to realize high-speed motion tracking in the embedded system. In order to further expand the application of the motion tracking technology, this paper introduces a two-step Kalman filter, which is suitable for the embedded system. The filter is composed of two sub filters, and is adaptively adjusted based on the variance matching of fuzzy logic. IMU orientation is calculated based on the filtered acceleration vector and the estimated yaw. This approach simplifies the mathematical model, reduces the matrix operations and improves the speed of computation.     

Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm

This paper addresses the problem of attitude estimation using low cost, small-sized inertial sensors under dynamic maneuvers. An adaptive complementary filter with fuzzy logic and simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed. By recognizing the situation of dynamic condition via fuzzy logic, the cut-off frequency of the complementary filter is determined adaptively under varying vehicle dynamics. Also, the SPSA algorithm is used to tune the parameters of fuzzy system. Simulation results based on the test data show that the proposed SPSA-based fuzzy complementary filter exhibits a significant performance improvement for attitude estimation during dynamic maneuvers.     

Estimation of intra-operative brain shift based on constrained Kalman filter

In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method.     

Intelligent fuzzy weighted input estimation method for the forces generated by an operating rotating machine

This paper presents a novel inverse method which efficiently and robustly estimates the vibration forces of a rotating machine mounted upon isolators under the operating condition. This new input estimation method includes the Kalman filter (KF) and the recursive least square estimator (RLSE), weighted by adopting a fuzzy weighting factor based on the fuzzy logic inference system. The excellent performance of this inverse method is demonstrated by solving the vibration problem and comparing it with constant weighting factors. The results reveal that this method has better feasibility and effectiveness in estimating vibratory forces.     

Implementation of a Kalman filter in positioning for autonomous vehicles, and its sensitivity to the process parameters

Modern autonomous vehicles are using more than one method for performing the positioning task. The most common positioning methods for indoor vehicles are odometry for relative positioning and triangulation for absolute positioning. In many cases a Kalman filter is required to merge the data from the positioning systems and determines the vehicle position based on error analysis of the measurements and calculation procedures. A Kalman filter is particularly advantageous for on-the-fly positioning, which is performed while the vehicle is in motion. This paper presents the implementation of a Kalman filter in ROBI — an AGV for material handling in a manufacturing environment. The performance of the filter in estimating the position of the AGV and the effect of motion parameters (speed, path curvature, beacon layout etc.) on filter accuracy are shown.     

基于卡尔曼滤波器的交流伺服系统自适应滑模控制

为了减小负载转矩扰动和系统参数摄动对永磁同步电机控制系统的影响,提出了一种基于卡尔曼滤波器的自适应滑模速度控制器。该控制器由自适应律估计参数摄动项,用卡尔曼滤波器估计外部扰动项。设计了含积分作用的滑模面,以保证电机转速的无静差跟踪;采用了指数趋近律,以提高趋近速度并削弱抖振。卡尔曼滤波器估计得到的系统外部扰动前馈补偿至控制器的输出,用于有效降低滑模控制器的不连续切换项造成的系统抖振。实验结果显示:跟踪设定的600r/min转速时,控制器稳态转速精度达到±1r/min。电机在以600r/min稳速运行时,设计的控制器在1.6N·m的外部转矩扰动下的最大转速波动比传统PI控制器的转速波动减小了2%。仿真分析和实验数据表明基于卡尔曼滤波器的自适应滑模控制器对交流伺服控制系统具有较强的抗扰动性、鲁棒性以及良好的稳态性能。     

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results.     

基于卡尔曼滤波的虚拟采样同步控制算法研究

随着工业的发展,以及对系统通用性和可靠性要求的提高,研究用多个电机共同完成流水线生产已经越来越具有实际工程意义。本文以行列式制瓶机中的同步控制为背景,针对接近开关检测装置不能实时提供相位信息,提出了一种基于卡尔曼滤波的虚拟采样同步控制算法,实现供料机、分料机、拨瓶机和输送机等的同步控制,同步控制精度可达1度以内。     

Estimation of spray flow characteristics using ensemble Kalman filter

Spray flows are widely used in several industrial applications, such as combustion engines. Accurate measurement of spray flow characteristics requires sophisticated equipment and techniques. In recent years, the discrete droplet method (DDM), which analyses droplet scattering, has become a mature technique and has been applied to various analyses. We propose an estimation system based on particle image velocimetry (PIV) measurements and an ensemble Kalman filter, together with DDM, to efficiently investigate spray flow characteristics. The proposed method performs data assimilation on the velocity distribution in a two-dimensional cross-section obtained by PIV to estimate the characteristics of the spray flow in three dimensions. In this study, the system was constructed so that droplet particle is ensembled during data assimilation to estimate the droplet diameter distribution indirectly. The proposed method can be used to estimate the spray velocity and droplet size distribution. The numerical solution obtained using DDM was used as a criterion for assimilation and validated by conducting twin experiments. The results showed that, in terms of spray velocity, the estimation error for the velocity component parallel to the main flow was 2% and that for the velocity component perpendicular to the main flow was around 10%. Finally, the velocity and particle size distributions of the spray stream and the three-dimensional droplet distribution were estimated by assimilating the velocity distributions measured by PIV. This technique predicts the spray angle and droplet size distribution from the two-dimensional velocity field of the PIV only and is expected to contribute to the development of injectors and atomizers.     

Image reconstruction algorithm of electromagnetic tomography based on fractional Kalman filter

Aiming at the problem of low quality of image reconstruction of electromagnetic tomography (EMT), in this paper, an image reconstruction algorithm of EMT based on fractional Kalman filter (FKF) is proposed. Firstly, the principle of EMT and the principle of state equation of FKF are expound respectively. FKF is often used in the state estimation of nonlinear systems. There is a nonlinear relationship between the object field distribution and the sensor signal in the EMT. Therefore, according to this feature, FKF is applied to the image reconstruction algorithm of EMT. The image reconstruction process of EMT is regarded as the state estimation process of FKF, the normalized measurement voltage is taken as the observation value, and the sensitivity matrix is taken as the measurement matrix. To establish the nonlinear state estimation equation of the FKF and a priori estimation error covariance equation in the EMT, the gray value of image obtained by LBP is used as the initial value of the state estimation, a prior estimation state vector and a priori estimation error covariance matrix are obtained by prediction update, the Kalman filter gain and the posterior estimation error covariance matrix are obtained by the correction feedback process. After repeated iterations, the final state vector, i.e. reconstructed image of EMT is obtained. Finally, simulation experiments are carried out for seven different flow patterns. The results show that the image error and correlation coefficients of the reconstructed image of this algorithm are better than traditional algorithms such as LBP, Landweber, Kalman filter, and have better anti-noise effect than Kalman filter. Therefore, the image reconstruction algorithm of FKF is a new method and means to study the image reconstruction of EMT.     

卡尔曼滤波在MEMS惯性姿态测量中的应用

由于测控成本和有效载荷的限制,一般采用微机电系统(MEMS)惯性传感器来测量小型无人机的飞行姿态。在MC9S12XS128单片机上通过嵌入式软件编程实现了卡尔曼滤波算法,并在JZJ-1型自准直仪转台上对MEMS加速度计和陀螺仪的输出信号进行了数据融合试验,较好地解决了MEMS惯性测量系统的零漂和机械振动干扰问题。     

基于离散Kalman滤波的桥梁结构振动信息处理技术研究

为获得较高精度的桥梁结构振动状态信息,提出先采用离散Kalman滤波对桥梁结构振动监测信息进行预处理,再用分布式Kalman动态数据融合算法进行融合。仿真结果表明该方法有效可行。     

LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

In this paper, to improve the positioning accuracy of LED chip, a dual rate adaptive fading Kalman filter algorithm with delay compensation is proposed and applied to the LED chip visual servo positioning system. Firstly, a structure of dual rate Kalman filter is introduced to the visual servo control system, which compensate the visual information delay and realize the accurate time sequential coordination of encoder and visual feedback. Then, considering the inaccuracy of system mathematical model, the adaptive forgetting factor is added to the iterative process of above algorithm, and the impact of accumulated model error on system stability is consequently mitigated. Finally, the experimental results show that the proposed method obviously decreases the positioning errors of LED chip and is robust to inaccuracy and uncertainty of system model parameters.     

基于EKF的异步电机转速和负载转矩估计

合理选择电机的容量具有重要的意义,电机的容量可根据电机的转速和负载转矩确定,将电机的转速和负载转矩同时作为系统的状态,提出了一种基于EKF同时估计异步电机转速和负载转矩的方法,建立了包含异步电机转速和负载转矩状态的系统模型,基于该模型用EKF实现了同时估计异步电机转速和负载转矩,仿真和实验验证了所提方法能以较高的精度同时估计出电机的转速和负载转矩.     

基于产品功能参数的模糊成本估计

本文采用模糊线性回归模型,建立了产品成本与功能参数之间的函数关系,并根据产品的功能参数估计产品成本。通过实际计算,取得了满意结果,该模型可用于系列产品生产中新产品的成本估计。     

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

The growing demand for hydrocarbon production has resulted in improved oilfield management using various control and optimization strategies. These strategies increasingly require downhole equipment to obtain real-time oil and gas production rates with sufficient spatial and temporal resolution. In particular, downhole multiphase metering can improve the production of horizontal wells by allocating the zones of oil, gas and water inflow. However, the existing downhole multiphase meters are expensive, inaccurate or accurate only within a limited operating range and therefore such monitoring is unrealistic.To overcome these problems one can use the so-called multiphase soft-sensors, i.e. estimating flow rates from conventional sensors (e.g. pressure gauges) in combination with a dynamic multiphase flow model. This methodology uses inverse modeling concepts to estimate flow rates that are not measured directly. Based on the analysis of the transient pressure response due to a rapid inflow, a real-time estimator is proposed, which uses a dynamic model of the multiphase wellbore flow and information from conventional pressure sensors. The feasibility of the proposed concept is assessed via simulation-based case studies both for noisy synthetic measurements and for artificial data generated by the OLGA simulator.     

基于Kalman滤波和短时自相关变换的电压暂降检测

为了在含有谐波和噪声的电网中准确检测电压暂降,将卡尔曼滤波器和短时自相关变换结合起来,对相邻工频周期的采样信号做差,再进行短时自相关变换,准确检测到了电压扰动,采用卡尔曼滤波器对基波幅值和相位进行了估计;采用离线计算增益矩阵K的方式减小了卡尔曼递推算法的运算量,并在稳态时通过采用定常增益矩阵K达到了抑制滤波发散的目的,同时减小了所需的存储空间;M atlab仿真实验证实了此方法的有效性。     

Very-low speed control of PMSM based on EKF estimation with closed loop optimized parameters

When calculating the speed from the position of permanent magnet synchronous motor (PMSM), the accuracy and real-time are limited by the precision of the sensor. This problem causes crawling and jitter at very-low speed. Using the angle from the position sensor, an extended Kalman filter (EKF) designed in dq-coordinate is presented to solve this problem. The usage of position sensor simplifies the model and improves the accuracy of speed estimation. Specially, a closed loop optimal (CLO) method is devised to overcome the difficulty to adjust the parameters of the EKF. The EKF is the feedback link of speed control, CLO method is derived from the perspective of the speed step response to optimize the measurement covariance matrix and the system covariance matrix of EKF. Simulation and experimental results, comparing the low-speed performance of the EKF and sensor feedback methods, prove the effectiveness of the method to adjust the parameters of EKF and the advantages in eliminating the low speed jitter.     

状态χ2检验法在惯性航姿系统内阻尼卡尔曼滤波器中的应用

本文探讨了如何利用惯性测量组合本身的信息来提高捷联航姿系统的姿态精度。根据平台式阻尼网络的思想，设计了捷联式内阻尼卡尔曼滤波器，将惯导系统捷联解算获得的姿态与加速度计估计的姿态进行组合，在系统非加速度状态下，提高了姿态输出的精度。为了实时监测系统的运动状态从而判断内阻尼姿态的有效性，本文成功将状态χ2检验法应用在内阻尼卡尔曼滤波器中，设计了基于2个状态传播器的故障监测器，并通过对故障检测向量元素的检验代替对整个向量的检验，提高了故障监测的灵敏度和可靠性。最后，实际系统的动静态实验验证了本文所提出的方法的有效性。