پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 438 |
| تعداد مقالات | 5,650 |
| تعداد مشاهده مقاله | 7,687,047 |
| تعداد دریافت فایل اصل مقاله | 6,341,330 |
تخمین سریع جهتگیری اولیه ناوبری اینرسی براساس فیلتر کالمن و مشاهدهگر مود لغزشی مرتبه بالا | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 9، دوره 53، شماره 6، شهریور 1400، صفحه 3571-3586 اصل مقاله (2.17 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2020.18407.6815 | ||
| نویسندگان | ||
| سعید خان کلانتری1؛ کاظم حیدری2؛ محسن حاجی زاده3؛ حسن محمدخانی* 4 | ||
| 1دانشکده برق، دانشگاه خواجه نصیرالدین طوسی، تهران، ایران | ||
| 2دانشکده برق، دانشگاه تربیت مدرس | ||
| 3دانشکده مهندسی مکانیک، دانشگاه امام حسین، تهران، ایران، | ||
| 4دانشکده هوا فضا، دانشگاه امام حسین، تهران، ایران | ||
| چکیده | ||
| سیستم ناوبری اینرسی یک سیستم ناوبری کور بوده و دقت آن کاملا به دقت تخمین شرایط اولیه و بهویژه جهتگیری اولیه وابسته است، به همین دلیل تعیین دقیق جهتگیری اولیه در یک سیستم ناوبری اینرسی بسیار مهم است. در این مقاله یک روش جدید برای افزایش دقت و سرعت همگرایی جهتگیری اولیه در یک سیستم ناوبری اینرسی ارائه شده است. روش ارائهشده در این مقاله یک روش دو مرحلهای است که شامل تلفیق فیلتر کالمن با مشاهدهگر مود لغزشی مرتبه بالا میباشد. در سیستم ناوبری اینرسی خطاهای ترازیابی بسیار سریعتر از خطای زاویه سمت همگرا میشوند، بنابراین همگرایی زاویه سمت زمان زیادی را به زمان تخمین جهتگیری اولیه تحمیل میکند. به همین دلیل در این مقاله در مرحله اول به کمک فیلتر کالمن متغیرهای حالت سیستم تخمین زده میشوند و پس از آنکه متغیرهای حالت به جز زاویه سمت همگرا شدند، مرحله دوم آغاز میشود. در مرحله دوم نتایج تخمین حاصل از فیلتر کالمن بهعنوان ورودی برای تعریف یک سیستم با ورودی نامعین، معادل با سیستم خطای ناوبری اینرسی به کار برده شده و با استفاده از مشاهدهگر مود لغزشی مرتبه بالا، متغیر زاویه سمت، از سیستم جدید با ورودی نامعین تخمین زده میشود. نتایج شبیهسازی نشان میدهد که استفاده از روش ارائهشده در این مقاله علاوه بر افزایش سرعت همگرایی باعث افزایش دقت تخمین نیز میشود. | ||
| کلیدواژهها | ||
| ناوبری اینرسی؛ جهتگیری اولیه؛ خطای زاویه سمت؛ فیلتر کالمن؛ مشاهدهگر مود لغزشی مرتبه بالا | ||
| عنوان مقاله [English] | ||
| Fast initial alignment for inertial navigation system based on high order sliding mode observer and Kalman filter | ||
| نویسندگان [English] | ||
| Saeed khankalantary1؛ ;azem heidari2؛ mohsen hajizadeh3؛ hasan mohammadkhani4 | ||
| 1Department of Electrical and Computer Engineering, khaje nasir University. | ||
| 2faculty of electrical engineering, tarbiat modares university | ||
| 3Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran. | ||
| 4Department of Aerospace Engineering, Imam Hossein University. | ||
| چکیده [English] | ||
| The inertial navigation system is a dead reckoning system, thus initial alignment for an inertial navigation system plays an important role in the accuracy of it. In this paper, a novel approach for initial alignment in an inertial navigation system with increased speed and accuracy is proposed. This method has two stages, which integrates the Kalman filter and a high order sliding mode observer. In the inertial navigation system, leveling misalignment angles reach the steady-state faster than the azimuth misalignment angle does, which means the azimuth alignment takes a considerable time for initial alignment. Therefore, in this paper at the first stage estimations of state variables of the system are obtained using the Kalman filter and whenever all variables (except azimuth alignment) reach steady-state, the second stage begins. In the second stage, the estimation which is obtained by the Kalman filter is used as the input to design an equivalent system with unknown inputs for inertial navigation system. A high-order sliding mode observer is then used to estimate the states of a system with an unknown input for estimating the azimuth alignment angle. This method not only increases the speed of estimation but also has comparable accuracy. | ||
| کلیدواژهها [English] | ||
| Inertial navigation, Initial alignment, Azimuth misalignment, Kalman filter, High order sliding mode observer | ||
| مراجع | ||
|
[1] R.M. Rogers, Applied mathematics in integrated navigation systems, American Institute of Aeronautics and Astronautics, 2007. [2] R.M. Rogers, Applied mathematics in integrated navigation systems. American Institute of Aeronautics and Astronautics, Inc., Reston, Virginia, USA, (2003). [3] D. Titterton, J.L. Weston, J. Weston, Strapdown inertial navigation technology, IET, 2004. [4] N. El-Sheimy, S. Nassar, A. Noureldin, Wavelet de-noising for IMU alignment, IEEE Aerospace and Electronic Systems Magazine, 19(10) (2004) 32-39. [5] R. Kalman, A new approach to linear filtering and prediction theory, Trans. ASME, J. Basic Eng., 83 (1961) 95-108. [6] J. Li, N. Song, G. Yang, R. Jiang, Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles, Review of Scientific Instruments, 87(7) (2016) 075118. [7] Y. Zhang, L. Luo, T. Fang, N. Li, G. Wang, An improved coarse alignment algorithm for odometer-aided sins based on the optimization design method, Sensors, 18(1) (2018) 195. [8] J.G. Park, J.G. Lee, C.G. Park, SDINS/GPS in-flight alignment using GPS carrier phase rate, GPS Solutions, 8(2) (2004) 74-81. [9] S. Han, J. Wang, A novel initial alignment scheme for low-cost INS aided by GPS for land vehicle applications, The Journal of Navigation, 63(4) (2010) 663-680. [10] F. Jiancheng, Y. Sheng, Study on innovation adaptive EKF for in-flight alignment of airborne POS, IEEE Transactions on Instrumentation and Measurement, 60(4) (2011) 1378-1388. [11] D. Gu, N. El-Sheimy, T. Hassan, Z. Syed, Coarse alignment for marine SINS using gravity in the inertial frame as a reference, in: 2008 IEEE/ION Position, Location and Navigation Symposium, IEEE, 2008, pp. 961-965. [12] P.M. Silson, Coarse alignment of a ship's strapdown inertial attitude reference system using velocity loci, IEEE Transactions on Instrumentation and Measurement, 60(6) (2011) 1930-1941. [13] K. Taizhong, F. Jiancheng, W. Wei, Quaternion-optimization-based in-flight alignment approach for airborne POS, IEEE Transactions on Instrumentation and Measurement, 61(11) (2012) 2916-2923. [14] J. Li, J. Xu, L. Chang, F. Zha, An improved optimal method for initial alignment, The Journal of Navigation, 67(4) (2014) 727-736. [15] Y. Wu, X. Pan, Velocity/position integration formula part I: Application to in-flight coarse alignment, IEEE Transactions on Aerospace and Electronic Systems, 49(2) (2013) 1006-1023. [16] L. Chang, J. Li, S. Chen, Initial alignment by attitude estimation for strapdown inertial navigation systems, IEEE Transactions on Instrumentation and Measurement, 64(3) (2014) 784-794. [17] G. Cheng, S. Cao, L. Guo, W. Chen, Initial alignment of Inertial Navigation System based on a predictive iterated Kalman filter, in: 2018 37th Chinese Control Conference (CCC), IEEE, 2018, pp. 4655-4660. [18] X. Xu, J. Lu, T. Zhang, A Fast-Initial Alignment Method With Angular Rate Aiding Based on Robust Kalman Filter, IEEE Access, 7 (2019) 51369-51378. [19] W. Li, W. Wu, J. Wang, L. Lu, A fast SINS initial alignment scheme for underwater vehicle applications, The Journal of Navigation, 66(2) (2013) 181-198. [20] Z. Chuanbin, T. Weifeng, J. Zhihua, A novel method improving the alignment accuracy of a strapdown inertial navigation system on a stationary base, Measurement Science and Technology, 15(4) (2004) 765. [21] X. Wang, Fast alignment and calibration algorithms for inertial navigation system, Aerospace Science and Technology, 13(4-5) (2009) 204-209. [22] Y. Huang, Y. Zhang, X. Wang, Kalman-filtering-based in-motion coarse alignment for odometer-aided SINS, IEEE Transactions on instrumentation and measurement, 66(12) (2017) 3364-3377. [23] T. Du, L. Guo, J. Yang, A fast initial alignment for SINS based on disturbance observer and Kalman filter, Transactions of the Institute of Measurement and Control, 38(10) (2016) 1261-1269. [24] M. Hou, R. Patton, Optimal filtering for systems with unknown inputs, IEEE transactions on Automatic Control, 43(3) (1998) 445-449. [25] Y. Cheng, H. Ye, Y. Wang, D. Zhou, Unbiased minimum-variance state estimation for linear systems with unknown input, Automatica, 45(2) (2009) 485-491. [26] J. NASH, R, J. D'APPOLITO, K. ROY, Error analysis of hybrid aircraft inertial navigation systems, in: Guidance and Control Conference, 1972, pp. 848. [27] F.J. Bejarano, L. Fridman, High order sliding mode observer for linear systems with unbounded unknown inputs, International Journal of Control, 83(9) (2010) 1920-1929. [28] K.R. Britting, Inertial navigation systems analysis, (1971). [29] C. Broxmeyer, C. Leondes, Inertial navigation systems, in, American Society of Mechanical Engineers Digital Collection, 1964. [30] I. Guidance, GR Pitman, Jr., Ed, in, John Wiley & Sons, Inc., New York, 1962. [31] C.T. Leondes, Guidance and control of aerospace vehicles, McGraw-Hill New York, 1963. [32] D.O. Benson, A comparison of two approaches to pure-inertial and Doppler-inertial error analysis, IEEE Transactions on Aerospace and Electronic Systems, (4) (1975) 447-455. [33] J.A. D'Appolito, The evaluation of Kalman filter designs for multisensor integrated navigation systems, Air Force Avionics Laboratory, 1971. [34] C. Hutchinson, H. Wondergem, An error analysis technique for inertial navigation systems and Kalman filters, MASSACHUSETTS UNIV AMHERST SCHOOL OF ENGINEERING, 1968. [35] A. Yavnai, I.Y. Bar-Itzhack, Self-contained updating of ground inertial navigation system, Israel Journal of Technology, 18 (1980) 304-313. [36] L. Fridman, A. Levant, J. Davila, High-order sliding modes observer for linear systems with unbounded unknown inputs, IFAC Proceedings Volumes, 42(17) (2009) 216-221. [37] F.J. Bejarano, L. Fridman, Unbounded unknown inputs estimation based on high-order sliding mode differentiator, in: Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, IEEE, 2009, pp. 8393-8398. [38] H.L. Trentelman, A.A. Stoorvogel, M. Hautus, Control theory for linear systems, Springer Science & Business Media, 2012. [39] B. Molinari, A strong controllability and observability in linear multivariable control, IEEE Transactions on Automatic Control, 21(5) (1976) 761-764. [40] A. Levant, Higher-order sliding modes, differentiation and output-feedback control, International journal of Control, 76(9-10) (2003) 924-941.
| ||
|
آمار تعداد مشاهده مقاله: 914 تعداد دریافت فایل اصل مقاله: 1,200 |
||