Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks
GyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/7/1692 |
| _version_ | 1798000057330434048 |
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| author | Yuyu Zhao Hui Zhao Xin Huo Yu Yao |
| author_facet | Yuyu Zhao Hui Zhao Xin Huo Yu Yao |
| author_sort | Yuyu Zhao |
| collection | DOAJ |
| description | GyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the problem of angular rate sensing with the GyroWheel is investigated. Firstly, a simplified rate sensing equation is introduced, and the error characteristics of the method are analyzed. According to the analysis results, a rate sensing principle based on torque balance theory is developed, and a practical way to estimate the angular rates within the whole operating range of GyroWheel is provided by using explicit genetic algorithm optimized neural networks. The angular rates can be determined by the measurable values of the GyroWheel (including tilt angles, spin rate and torque coil currents), the weights and the biases of the neural networks. Finally, the simulation results are presented to illustrate the effectiveness of the proposed angular rate sensing method with GyroWheel. |
| first_indexed | 2024-04-11T11:14:12Z |
| format | Article |
| id | doaj.art-8b93ed4e45e447a3937d6ccc8f67a2b6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:14:12Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b93ed4e45e447a3937d6ccc8f67a2b62022-12-22T04:27:19ZengMDPI AGSensors1424-82202017-07-01177169210.3390/s17071692s17071692Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural NetworksYuyu Zhao0Hui Zhao1Xin Huo2Yu Yao3Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, ChinaControl and Simulation Center, Harbin Institute of Technology, Harbin 150080, ChinaControl and Simulation Center, Harbin Institute of Technology, Harbin 150080, ChinaControl and Simulation Center, Harbin Institute of Technology, Harbin 150080, ChinaGyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the problem of angular rate sensing with the GyroWheel is investigated. Firstly, a simplified rate sensing equation is introduced, and the error characteristics of the method are analyzed. According to the analysis results, a rate sensing principle based on torque balance theory is developed, and a practical way to estimate the angular rates within the whole operating range of GyroWheel is provided by using explicit genetic algorithm optimized neural networks. The angular rates can be determined by the measurable values of the GyroWheel (including tilt angles, spin rate and torque coil currents), the weights and the biases of the neural networks. Finally, the simulation results are presented to illustrate the effectiveness of the proposed angular rate sensing method with GyroWheel.https://www.mdpi.com/1424-8220/17/7/1692GyroWheelangular rate sensinglarge tilt anglesgenetic algorithmartificial neural network |
| spellingShingle | Yuyu Zhao Hui Zhao Xin Huo Yu Yao Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks Sensors GyroWheel angular rate sensing large tilt angles genetic algorithm artificial neural network |
| title | Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks |
| title_full | Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks |
| title_fullStr | Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks |
| title_full_unstemmed | Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks |
| title_short | Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks |
| title_sort | angular rate sensing with gyrowheel using genetic algorithm optimized neural networks |
| topic | GyroWheel angular rate sensing large tilt angles genetic algorithm artificial neural network |
| url | https://www.mdpi.com/1424-8220/17/7/1692 |
| work_keys_str_mv | AT yuyuzhao angularratesensingwithgyrowheelusinggeneticalgorithmoptimizedneuralnetworks AT huizhao angularratesensingwithgyrowheelusinggeneticalgorithmoptimizedneuralnetworks AT xinhuo angularratesensingwithgyrowheelusinggeneticalgorithmoptimizedneuralnetworks AT yuyao angularratesensingwithgyrowheelusinggeneticalgorithmoptimizedneuralnetworks |