A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm
Source abstract
Abstract
The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation orientation and angle. The basic idea is to embed a PM on a ball while the Hall sensors are arrayed into the ball socket. When the ball rotates, the Hall sensor array detects the variation of the magnetic induction intensity in space. By establishing a mathematical model between the variation of the magnetic induction intensity and the orientation and angle of rotation, the rotation angle in the space where the ball is located can be inversely solved. The establishment of the theoretical model is based on the theory of the equivalent magnetic charge method, which has a few native defects that cannot be overcome by itself. This paper presents the relationship between the magnetic induction intensity change and the rotation angle of the ball in space, which was constructed by an artificial neural network (ANN) and will simplify the mathematical model, shorten the operation time, and improve the efficiency of real-time detection. Based on the simulation analysis, the optimal matching scheme between the PM and the magnetic effect sensor was determined, and the structural parameters of the ball joint prototype were optimized. The data training and comparison test of the neural network model were completed on a self-developed calibration device. The experimental results show that for a ±20° measurement range, the average errors of the uniaxial measurements are 1′51″ and 1′55″ on the two axes, respectively. At present, the measurement accuracy of the prototype is still relatively low; however, this idea of modeling based on ANN removes the shackles of mathematical modeling, reminding us that we can consider the design of sensors or complete geometric measurement modeling from a new perspective.
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Research context
OpenAlex topics: Inertial Sensor and Navigation · Sensor Technology and Measurement Systems · Astronomical Observations and Instrumentation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, China
Funding
National Natural Science Foundation of China
References (18)
Show source-provided references
1. Measurement method of rotation angle and clearance in intelligent spherical hinge10.1088/1361-6501/aab705 ↗
2. Hu, P., and Wang, L. (2015). A measurement method for ball joint spatial rotation angle. Sens. Mater., 27.
3. Non-spherical ball-socket joint design for Delta-type robots10.1016/j.mechatronics.2017.05.003 ↗
4. Analysis of Static and Dynamic Characteristic of Hydrostatic Spherical Hinge10.1115/1.4028910 ↗
5. Problems of artificial neural network10.1016/j.jss.2017.07.017 ↗
6. Artificial neural networks used in optimization problems10.1016/j.neucom.2017.04.075 ↗
7. Efficient deep features selections and classification for flower species recognition10.1016/j.measurement.2019.01.041 ↗
8. Application of Artificial Neural Network and Empirical Mode Decomposition for Predications of Hourly Values of Active Power Consumption10.1007/978-3-030-02574-8_8 ↗
9. Short-term load forecasting in large scale electrical utility using artificial neural network
10. Petrovskiy, D., Barashkov, A., Sobolevsky, V., Sokolov, B., and Pjatkov, V. (2018, January 17–19). On the real time logistics monitoring system development using artificial neural network. Proceedings of the 20th International Conference on Harbor, Maritime & Multimodal Logistics Modelling and Simulation (HMS2018), Budapest, Hungary.
11. Flavors mapping by Kohonen network classification of Panel Tests of Extra Virgin Olive Oil10.1016/j.measurement.2015.09.051 ↗
12. Caciotta, M., Giarnetti, S., and Leccese, F. (2009, January 6–11). Hybrid neural network system for electric load forecasting of telecomunication station. Proceedings of the 19th IMEKO XIX World Congress, Lisbon, Portugal.
13. A constrained optimization method based on BP neural network
14. An RBF collocation method for solving optimal control problems10.1016/j.robot.2016.10.015 ↗
15. Gesture recognition based on BP neural network improved by chaotic genetic algorithm10.1007/s11633-017-1107-6 ↗
16. Research on FBG-Based CFRP Structural damage edentification using BP neural network10.1007/s13320-018-0466-0 ↗
17. Adaptive Chaotic Prediction Algorithm of RBF Neural Network Filtering Model Based on Phase Space Reconstruction10.4304/jcp.8.6.1449-1455 ↗
18. An incremental learning algorithm for the hybrid RBF-BP network classifier10.1186/s13634-016-0357-8 ↗