Integral based sliding mode stabilizing a camera platform using Kalman filter attitude estimation

Mechatronics ◽  
2017 ◽  
Vol 44 ◽  
pp. 42-51 ◽  
Author(s):  
Mohammad Sheikh Sofla ◽  
Mohammad Zareinejad ◽  
Mohsen Parsa ◽  
Hassan Sheibani
Keyword(s):  
Kalman Filter ◽  
Sliding Mode ◽  
Attitude Estimation

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

Steerable Needle Trajectory Following in the Lung: Torsional Deadband Compensation and Full Pose Estimation With 5DOF Feedback for Needles Passing Through Flexible Endoscopes

2020 ◽  
Author(s):  
Tayfun Efe Ertop ◽  
Maxwell Emerson ◽  
Margaret Rox ◽  
Josephine Granna ◽  
Robert Webster ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sliding Mode ◽  
Ex Vivo ◽  
Flexible Bronchoscope ◽  
Complex Interactions ◽  
Trajectory Following ◽  
Steerable Needles ◽  
Needle Trajectory ◽  
Following Error ◽  
Steerable Needle

Abstract Bronchoscopic diagnosis and intervention in the lung is a new frontier for steerable needles, where they have the potential to enable minimally invasive, accurate access to small nodules that cannot be reliably accessed today. However, the curved, flexible bronchoscope requires a much longer needle than prior work has considered, with complex interactions between the needle and bronchoscope channel, introducing new challenges in steerable needle control. In particular, friction between the working channel and needle causes torsional windup along the bronchoscope, the effects of which cannot be directly measured at the tip of thin needles embedded with 5 degree-of-freedom magnetic tracking coils. To compensate for these effects, we propose a new torsional deadband-aware Extended Kalman Filter to estimate the full needle tip pose including the axial angle, which defines its steering direction. We use the Kalman Filter estimates with an established sliding mode controller to steer along desired trajectories in lung tissue. We demonstrate that this simple torsional deadband model is sufficient to account for the complex interactions between the needle and endoscope channel for control purposes. We measure mean final targeting error of 1.36 mm in phantom tissue and 1.84 mm in ex-vivo porcine lung, with mean trajectory following error of 1.28 mm and 1.10 mm, respectively.

scholarly journals Attitude estimation of a motorcycle via Unscented Kalman Filter

2010 ◽  
Vol 43 (18) ◽  
pp. 511-516 ◽  
Author(s):  
Stefano Corbetta ◽  
Ivo Boniolo ◽  
Sergio M. Savaresi
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Attitude Estimation

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Sliding mode control based on Kalman filter dynamic estimation of battery SOC

2018 ◽  
Author(s):  
Dongmeia He ◽  
Enguang Hou ◽  
Xin Qiao ◽  
Guangmin Liu
Keyword(s):  
Kalman Filter ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Mode Control ◽  
Dynamic Estimation

Spacecraft Attitude Estimation using Huber-based Kalman Filter

2014 ◽  
Vol 13 (6) ◽  
pp. 1234-1239
Author(s):  
Wei Li ◽  
Meihong Liu ◽  
Deren Gong ◽  
Dengping Duan
Keyword(s):  
Kalman Filter ◽  
Attitude Estimation ◽  
Spacecraft Attitude

Adaptive sliding mode control for AC servo system based on Kalman filter

2014 ◽  
Vol 22 (8) ◽  
pp. 2088-2095 ◽  
Author(s):  
邓永停 DENG Yong-ting ◽  
李洪文 LI Hong-wen ◽  
王建立 WANG Jian-li ◽  
贾建禄 JIA Jian-lu
Keyword(s):  
Kalman Filter ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Servo System ◽  
Adaptive Sliding Mode Control ◽  
Ac Servo System ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Ac Servo

scholarly journals A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5459 ◽  
Author(s):  
Xuliang Lu ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Haifeng Yan ◽  
Lei Si ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Hydraulic Support ◽  
Sensing System ◽  
Rock Structure ◽  
Gradient Descent Algorithm ◽  
Yaw Angle

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

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