scholarly journals Optimization of a Suspension of a Micromechanical Accelerometer in an Angular Velocity Measurement Unit

2022 ◽  
Vol 1215 (1) ◽  
pp. 012011
Author(s):  
I A Iznairov ◽  
M V Efremov ◽  
M A Barulina
Keyword(s):  
Angular Velocity ◽  
Final Stage ◽  
Velocity Measurement ◽  
Measurement Unit ◽  
Shock Loads ◽  
Technical Requirements ◽  
Micromechanical Accelerometer ◽  
Unit Shell

Abstract Optimization of the suspension of the micromechanical accelerometer, which is a part of an angular velocity measurement unit, was considered. This problem was faced because of the need to improve the design of the existing device to meet new technical requirements. Shock loads up to 1 000 g exceed ultimate loads for micromechanical accelerometers by several orders of magnitude. During the study, it turned out that the measurement unit shell works as a resonator, so the shock loads on the micromechanical accelerometer can even exceed 1 000g. In this case, suspensions of the micromechanical accelerometer are deformed in the plane of the accelerometer, which causes their destruction. The solution to this problem was hampered by the strict limits on the overall dimensions of the device. The existing shock dampers that could absorb such shock loads simply did not fit into the attachment points. Therefore, an original spring was designed and manufactured for this purpose. At the final stage, an additional system of passive heat outlets from the accelerometer to the device shell was designed and installed.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

scholarly journals Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

2020 ◽  
pp. 002029402091770
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Weixing Qian ◽  
Yang Jin ◽  
Pei Qi
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Angular Rate ◽  
The State ◽  
Measurement Unit ◽  
Fusion Method ◽  
Microelectromechanical System ◽  
Inertial Measurement

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

scholarly journals Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Miaoxin Ji ◽  
Jinhao Liu ◽  
Xiangbo Xu ◽  
Yuyang Guo ◽  
Zhenchun Lu
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Gradient Descent ◽  
Measurement Noise ◽  
Measurement Unit ◽  
Alignment Algorithm ◽  
Extended Kalman Filtering ◽  
Attitude Error ◽  
Gradient Descent Algorithm

The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

scholarly journals Implementation of the centralized control system for drone training

2018 ◽  
Vol 7 (3.3) ◽  
pp. 379
Author(s):  
Bong Hyun Kim
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Embedded System ◽  
Flight Control ◽  
Attitude Control ◽  
Test System ◽  
Measurement Unit ◽  
Centralized Control ◽  
Structural Problems ◽  
Earth Magnetic Field

Background/Objectives: The drones became a representative item in the IoT era. However, there is no drone pilot test system that can safely train this in the education field. Drones have very dangerous structural problems, so it is very necessary to practice them easily. Therefore, it is necessary to develop a system that can control the drones safely and easily while controlling them.Methods/Statistical analysis: In this paper, we will develop software for controlling a dedicated board platform that can securely perform ground testing by mounting four drones of motor and drive on a board (PCB). To this end, we supported various control IMU (Inertial Measurement Unit) boards for attitude control by using sensor which is the core technology of drone flight control. Also, Acceleration Data, Angular Velocity Data, Earth Magnetic Field Data, and Atmospheric Pressure Data for maintaining the altitude were used for the drone flight.Findings: In the implemented central control system, the AT chip is built in and designed to perform all control related to the flight of the drone. In addition, since it is an embedded system, we have programmed the attitude control using the sensor, the motor output setting, and the controller connection information. The CPU required for drones control can be replaced with various types of controllers besides Fno Arduino, UNO, Muiltiwii. For this purpose, the main PCB is designed so that the power supply terminal can be used for each CPU. Finally, it was developed as a setup program to correct the sensor and output of the drone.Improvements/Applications: The system implemented in this paper can easily control the drone. In addition, acceleration, angular velocity, geomagnetic field, air pressure sensor, GPS, etc. necessary for drone control can be utilized by stabilizing the initial set value. In other words, the zero point of the sensor can be captured and the signal appropriate to the current state of the drone can be stored in the processor.  

21620 Micro Gyroscopic Sensor using Active Magnetic Bearing : 3rd Report: Estimation of Tow-axis Angular Velocity Measurement in Frequency Domain

2007 ◽  
Vol 2007.13 (0) ◽  
pp. 17-18
Author(s):  
Yutaka MARUYAMA ◽  
Takeshi MIZUNO ◽  
Yuji ISHINO ◽  
Masaya TAKASAKI ◽  
Takayuki ISHIGAMI ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Frequency Domain ◽  
Velocity Measurement ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing

scholarly journals Design and Simulation of Two-Wheeled Balancing Mobile Robot with PID Controller

2020 ◽  
Vol 3 (1) ◽  
pp. 12-19
Author(s):  
Vicky Mudeng ◽  
Barokatun Hassanah ◽  
Yun Tonce Kusuma Priyanto ◽  
Okcy Saputra ◽  
◽  
...  
Keyword(s):  
Angular Velocity ◽  
Pid Controller ◽  
Pulse Width Modulation ◽  
Angular Acceleration ◽  
Electronic System ◽  
Measurement Unit ◽  
Dc Motors ◽  
Control Optimization ◽  
Working Principle ◽  
Balancing Robot

Mobile transportation robots using two wheels have now been investigated. The work within this study is to design and simulate two-wheeled robots, thus it can maintain its balance. Many control methods are used to determine satisfactory control optimization, therefore a proper response is obtained by sensor recitation corresponding with the reaction of a Direct Current (DC) motor. Recently, two-wheeled transportation robot is a Segway model. In this study, we apply a Proportional Integral Derivative (PID) controller as a control system in a self-balancing robot with a working principle is similar to an inverted pendulum. In the next study, the PID controller and the whole system are applied in the microcontroller board. The angular velocity of two DC motors used as a plant can be adjusted by Pulse Width Modulation (PWM) through a motor driver. An Inertial Measurement Unit (IMU) sensor is utilized to detect the angular acceleration and angular velocity of the self-balancing robot. The phase design is constructed by planning the robot dimension, mechanical system, and an electronic system. Particularly, this study performs mathematical modeling of the robot system to obtain the transfer function. In addition, we simulate the PID parameter with multiplication between the basic parameter and several fixed constants. The simulation results indicate that the robot can maintain its balance and remains perpendicularly stable for balancing itself.

High-sensitivity angular velocity measurement based on bidirectional coupled optoelectronic oscillator

2019 ◽  
Vol 440 ◽  
pp. 201-206 ◽  
Author(s):  
Lingyun Ye ◽  
Yanna Ma ◽  
Wenrui Wang ◽  
Ju Wang ◽  
Jinlong Yu ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Velocity Measurement ◽  
High Sensitivity ◽  
Optoelectronic Oscillator
Sign in / Sign up

Export Citation Format

Share Document