scholarly journals Comparison of Pixel-based Position Input and Direct Acceleration Input for Virtual Stick Balancing Tests

2020 ◽  
Vol 64 (2) ◽  
pp. 120-127
Author(s):  
Balazs A. Kovacs ◽  
Tamas Insperger
Keyword(s):  
Input Signal ◽  
Inertial Measurement Unit ◽  
Feedback Loop ◽  
Development Process ◽  
Measurement Unit ◽  
Input Device ◽  
Computer Mouse ◽  
Direct Measurements ◽  
Cursor Position ◽  
Crank Mechanism

A virtual stick balancing environment is developed using a computer mouse as input device. The development process is presented both on the hardware and software level. Two possible concepts are suggested to obtain the acceleration of the input device: discrete differentiation of the cursor position measured in pixels on the screen and by direct measurements via an Inertial Measurement Unit (IMU). The comparison of the inputs is carried out with test measurements using a crank mechanism. The measured signals are compared to the prescribed motion of the mechanism and it is shown that the IMU-based input signal fits better to the prescribed motion than the pixel-based input signal. The pixel-based input can also be applied after additional filtering, but this presents an extra computational delay in the feedback loop.

scholarly journals A correction in feedback loop applied to two-axis gimbal stabilization

2015 ◽  
Vol 63 (1) ◽  
pp. 217-219
Author(s):  
C. Zych ◽  
A. Wrońska-Zych ◽  
J. Dudczyk ◽  
A. Kawalec
Keyword(s):  
Working Conditions ◽  
Input Signal ◽  
Pid Controller ◽  
Feedback Loop ◽  
Measurement Unit ◽  
Observation System ◽  
Proportional Integral Derivative ◽  
Earned Value ◽  
Angular Velocities ◽  
Measurement Units

Abstract A two-axis gimbal system can be used for stabilizing platform equipped with observation system like cameras or different measurement units. The most important advantageous of using a gimbal stabilization is a possibility to provide not disturbed information or data from a measurement unit. This disturbance can proceed from external working conditions. The described stabilization algorithm of a gimbal system bases on a regulator with a feedback loop. Steering parameters are calculated from quaternion transformation angular velocities received from gyroscopes. This data are fed into the input of Proportional Integral Derivative (PID) controller. Their input signal is compared with earned value in the feedback loop. The paper presents the way of increasing the position’s accuracy by getting it in the feedback loop. The data fusion from a positioning sensor and a gyroscope results in much better accuracy of stabilization.

Control Limitations on Static Unstable Airframe Using Two-Loop Acceleration Autopilot

2011 ◽  
Vol 383-390 ◽  
pp. 4385-4390
Author(s):  
Jiang Wang ◽  
De Fu Lin ◽  
Jun Fang Fan
Keyword(s):  
High Frequency ◽  
Inertial Measurement Unit ◽  
Feedback Loop ◽  
Measurement Unit ◽  
Flight Performance ◽  
Actuator Dynamics ◽  
Rate Feedback ◽  
Stabilization Condition ◽  
Lever Effect ◽  
Loop Acceleration

An analysis for controlling a static-unstable tactical missile using two-loop acceleration autopilot was detailed. The rate feedback loop was firstly presented. The equivalent actuator dynamics was introduced and examined. Thus an overall stabilization condition combined with both low and high frequency cases was proposed. The lever effect led by inertial measurement unit was of benefit to a great controllable range. The results show that the autopilot control capacity is dominated by actuator bandwidth, and a compromise should be determined between the flight performance and the actuator requirement for a static unstable tactical missile.

Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

2017 ◽  
Vol 870 ◽  
pp. 79-84
Author(s):  
Zhen Xian Fu ◽  
Guang Ying Zhang ◽  
Yu Rong Lin ◽  
Yang Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Propagation Model ◽  
Measurement Unit ◽  
Input Device ◽  
Operational Environment ◽  
Inertial Measurement ◽  
Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

Optical Computer Mouse Referenced Calibration of an Inertial Measurement Unit for Use in Unmanned Underwater Vehicles

2014 ◽  
Vol 664 ◽  
pp. 274-278
Author(s):  
Serhat Ikizoğlu ◽  
Yaver Kamer
Keyword(s):  
Neural Network ◽  
Inertial Measurement Unit ◽  
Underwater Vehicles ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Unmanned Underwater Vehicles ◽  
Computer Mouse ◽  
Dynamic Errors ◽  
Optical Computer ◽  
Nonlinear Estimators

In this study an inertial measurement unit (IMU) used in unmanned underwater vehicles has been taken into consideration.The main objective of this study is to improvethe measurements obtained from an IMU used in the position detection by minimizing the effect of its static and dynamic errors on the output. To enhance the IMU data optical computer mouse (OCM) is proposed as calibrator. The data received from the OCM is used to train an artificial neural network (ANN) which would improve the IMU outputs by trying to estimate the reference data from the actual sensor outputs. The ANN performance is compared with that of classic low pass filtering methods to provide a relative performance criterion. The ANN trained with OCM data has given satisfactory results. During the training of ANNs the effects of several parameters such as neural network architecture, activation functions, training algorithm, layer and cell number have been investigated. Thus, the results, findings and insights obtained in this study can be applied in research areas where this kind of nonlinear estimators are used.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals The Accelerations of a Wave Measurement Buoy Impacted by Breaking Waves in the Surf Zone

2021 ◽  
Vol 9 (2) ◽  
pp. 214
Author(s):  
Adam C. Brown ◽  
Robert K. Paasch
Keyword(s):  
Inertial Measurement Unit ◽  
Surf Zone ◽  
Spherical Wave ◽  
Breaking Waves ◽  
Measurement Unit ◽  
High Fidelity ◽  
Wave Measurement ◽  
Near Shore ◽  
Shoaling Waves ◽  
Multiple Deployments

A spherical wave measurement buoy capable of detecting breaking waves has been designed and built. The buoy is 16 inches in diameter and houses a 9 degree of freedom inertial measurement unit (IMU). The orientation and acceleration of the buoy is continuously logged at frequencies up to 200 Hz providing a high fidelity description of the motion of the buoy as it is impacted by breaking waves. The buoy was deployed several times throughout the winter of 2013–2014. Both moored and free-drifting data were acquired in near-shore shoaling waves off the coast of Newport, OR. Almost 200 breaking waves of varying type and intensity were measured over the course of multiple deployments. The characteristic signature of spilling and plunging breakers was identified in the IMU data.

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