Tire Lateral Force Estimation System Using Nonlinear Kalman Filter

Motility is an important property of immune system cells. To describe cell motility, we use a continuous stochastic process and estimate its parameters and driving force based on a maximum likelihood approach. In order to improve the convergence of the maximization procedure, we use expectation-maximization (EM) iterations. The iterations include numerical maximization and the Kalman filter. To illustrate the method, we use cell tracks obtained from the intravital video microscopy of a zebrafish embryo.

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Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4036546 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 8

Author(s):

Seyed Fakoorian ◽

Vahid Azimi ◽

Mahmoud Moosavi ◽

Hanz Richter ◽

Dan Simon

Keyword(s):

Kalman Filter ◽

Continuous Time ◽

Lyapunov Functions ◽

Unscented Kalman Filter ◽

Estimation Error ◽

Reaction Force ◽

Force Estimation ◽

Joint Angles ◽

Estimation Errors ◽

Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

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Development of a Low-Cost Automated Tension Estimation System for Cable-Stayed Bridges

Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2 ◽

10.1115/smasis2008-614 ◽

2008 ◽

Cited By ~ 1

Author(s):

Soojin Cho ◽

Jerome Peter Lynch ◽

Chung-Bang Yun

Keyword(s):

Low Cost ◽

Tension Force ◽

Modal Parameters ◽

Wireless Sensing ◽

Force Estimation ◽

Cable Tension ◽

Embedded Algorithms ◽

Estimation System ◽

Automated Software ◽

Cable Stayed Bridges

Cable tension force is one of the most important structural parameters to monitor in cable-stayed bridges. For example, cable tension needs to be monitored during construction and maintenance to ensure the bridge is not overloaded. To economically monitor tension forces, this study proposes the use of an automated wireless tension force estimation system (WFTES) developed solely for cable force estimation. The design of the WFTES system can be divided into two parts: low-cost hardware and automated software. The low-cost hardware consists of an integrated platform containing a wireless sensing unit constructed from commercial off-the-shelf components, a low-cost commercial MEMS accelerometer, and a signal conditioning board for signal amplification and filtering. With respect to the automated software, a vibration-based algorithm using estimated modal parameters and information on the cable sag and bending stiffness is embedded into the wireless sensing unit. Since modal parameters are inputs to the algorithm, additional algorithms are necessary to extract modal features from measured cable accelerations. To validate the proposed WFTES, a scaled-down cable model was constructed in the laboratory using steel rope wire. The wire was exposed to broad-band excitations while the WFTES recorded the cable response and embedded algorithms interrogated the measured acceleration to estimate tension force. The results reveal the embedded algorithms properly identify the lower natural frequencies of the cable and make accurate estimates of cable tension. This paper concludes with a summary of the salient research findings and suggestions for future work.

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Control performance simulation of a space robotic joint by estimation of motor fast shaft position and speed via a seven-state nonlinear Kalman Filter

Aerotecnica Missili & Spazio ◽

10.1007/bf03404662 ◽

2013 ◽

Vol 92 (1-2) ◽

pp. 41-51

Author(s):

Piergiovanni Magnani ◽

Franco Bernelli-Zazzera

Keyword(s):

Kalman Filter ◽

Control Performance ◽

Performance Simulation ◽

Nonlinear Kalman Filter

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Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Sensors ◽

10.3390/s20082365 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2365

Author(s):

Danhe Chen ◽

K. A. Neusypin ◽

M. S. Selezneva

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Underwater Vehicle ◽

Measurement Information ◽

Nonlinear Controller ◽

Unmanned Underwater Vehicle ◽

Measuring Complex ◽

Nonlinear Kalman Filter

More accurate navigation systems are always required for autonomous unmanned underwater vehicles (AUUV)s under various circumstances. In this paper, a measuring complex of a heavy unmanned underwater vehicle (UUV) was investigated. The measuring complex consists of an inertial navigation platform system, a Doppler lag (DL) and an estimation algorithm. During a relatively long-term voyage of an UUV without surfacing and correction from buoys and stationary stations, errors of the measuring complex will increase over time. The increase in errors is caused by an increase in the deviation angles of the gyro platform relative to the accompanying trihedron of the selected coordinate system. To reduce these angles, correction is used in the structure of the inertial navigation system (INS) using a linear regulator. To increase accuracy, it is proposed to take into account the nonlinear features of INS errors; an adaptive nonlinear Kalman filter and a nonlinear controller were used in the correction scheme. Considering that, a modified nonlinear Kalman filter and a regulator in the measuring complex are proposed to improve the accuracy of the measurement information, and modification of the nonlinear Kalman filter was performed through a genetic algorithm, in which the regulator was developed by the State Dependent Coefficient (SDC) method of the formulated model. Modeling combined with a semi-natural experiment with a real inertial navigation system for the UUV demonstrated the efficiency and effectiveness of the proposed algorithms.

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