A new fuzzy logic-based adaptive complementary filter algorithm for UAV attitude estimation

Year 2024, Volume: 30 Issue: 3, 324 - 332, 29.06.2024

Ömer Karal , Hasan Kazdal

Abstract

Micro Electro-Mechanical System (MEMS) Based Inertial Measurement Units (IMU) are widely used for attitude estimation in unmanned aerial vehicle (UAV) systems owing to their small, light weight and cost effectiveness. On the other hand, it has some disadvantages that influence performance, such as noisy output, low sensitivity, poor accuracy, and bias stability. Also, MEMS-based IMU sensors (accelerometers and magnetometers and gyroscopes) cannot provide adequate navigation solutions as a standalone system. Different sensor fusion techniques have been proposed in the literature to obtain reliable attitude estimation. However, most of these fail in situations such as nonlinear measurement models, nonlinear process dynamics, and long-range navigation. This article presents a new fuzzy rule-based complementary filter (CF) that combines magnetic field, angular velocity and acceleration measurements from low-cost MEMS-based IMU sensors to achieve a more robust attitude estimation in a UAV under dynamic motion. The proposed approach adjusts the cut-off frequency of the CF to the optimum value according to the variable dynamic motion of the system. Thus, the problem of constant cut-off frequency is eliminated and a more robust attitude estimation is achieved even with the varying movements of the system. Both real experiments and numerical simulations confirm the validity of the presented method.

Keywords

Attitude estimation, Inertial measurement unit, Fuzzy Logic, Complementary filter, Real time experiments

İHA tutum tahmini için yeni bir bulanık mantık tabanlı uyarlanabilir tamamlayıcı filtre algoritması

Abstract

Mikro Elektro-Mekanik Sistem (MEMS) Tabanlı Atalet Ölçüm Birimleri (IMU), küçük, hafif ve maliyet etkinliği nedeniyle insansız hava aracı (İHA) sistemlerinde tutum tahmini için yaygın olarak kullanılmaktadır. Öte yandan, gürültülü çıkış, düşük hassasiyet, zayıf doğruluk ve önyargı kararlılığı gibi performansı etkileyen bazı dezavantajları vardır. Ayrıca, MEMS tabanlı IMU sensörleri (ivmeölçerler ve manyetometreler ve jiroskoplar) bağımsız bir sistem olarak yeterli navigasyon çözümleri sağlayamaz. Güvenilir tutum tahmini elde etmek için literatürde farklı sensör füzyon teknikleri önerilmiştir. Ancak, bunların çoğu, doğrusal olmayan ölçüm modelleri, doğrusal olmayan süreç dinamikleri ve uzun menzilli gezinme gibi durumlarda başarısız olur. Bu çalışma, dinamik hareket altındaki bir İHA'da daha gürbüz bir tutum tahmini başarmak için düşük maliyetli MEMS tabanlı IMU sensörlerinden alınan manyetik alan, açısal hız ve ivme ölçümlerini birleştiren yeni bir bulanık kural tabanlı tamamlayıcı filtre sunmaktadır. Önerilen yaklaşım, sistemin değişken dinamik hareketine göre tamamlayıcı filtrenin kesme frekansını optimum değere ayarlar. Böylece sabit kesme frekansı sorunu ortadan kaldırılır ve sistemin değişen hareketlerinde bile daha sağlam bir tutum tahmini elde edilir. Hem gerçek deneyler hem de sayısal simülasyonlar, sunulan yöntemin geçerliliğini doğrulamaktadır.

Keywords

Tutum tahmini, Atalet ölçüm birimi, Bulanık mantık, Tamamlayıcı filtre, Gerçek zamanlı deneyler

References

• [1] Altun M, Türker M. “Çok yüksek çözünürlüklü renkli İHA görüntülerinden kentsel alanlarda araç tespiti”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 371-384, 2020.
• [2] Halat M, Özkan Ö. “The optimization of UAV routing problem with a genetic algorithm to observe the damages of possible Istanbul earthquake”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 187-198, 2021.
• [3] Dong M, Yao G, Li J, Zhang L. “Calibration of low cost IMU’s inertial sensors for improved attitude estimation”. Journal of Intelligent & Robotic Systems, 100(3), 1015-1029, 2020.
• [4] Dudush A, Tyutyunnik V, Trofymov I, Bortnovs’Kiy S, Bondarenko S. “State of the art and problems of defeat of low, slow and small unmanned aerial vehicles”. Advances in Military Technology, 13(2), 157-171, 2018.
• [5] Candan B, Soken HE. “Robust attitude estimation using imu-only measurements”. IEEE Transactions on Instrumentation and Measurement, 70, 1-9, 2021.
• [6] Aparna GJ, Kamal C, Motta RN. “IMU based attitude estimation using adaptive complimentary filter”. IEEE International Conference on Communication information and Computing Technology, Mumbai, India, 25-27 June 2021.
• [7] Chon SJ, Choi JK, Kim JH. “Decision of complementary filter coefficient through real-time error analysis for attitude estimation using an IMU”. Journal of Advanced Marine Engineering and Technology, 45(5), 300-306, 2021.
• [8] Wu J, Zhou Z, Gao B, Li R, Cheng Y, Fourati H. “Fast linear quaternion attitude estimator using vector observations”. IEEE Transactions on Automation Science and Engineering, 15(1), 307-319, 2017.
• [9] Lyu F, Xu X, Zha X. An adaptive gradient descent attitude estimation algorithm based on a fuzzy system for UUVs. Ocean Engineering, 266, 1-12, 2022.
• [10] Hua MD, Ducard G, Hamel T, Mahony R, Rudin K. “Implementation of a nonlinear attitude estimator for aerial robotic vehicles”. IEEE Transactions on Control Systems Technology, 22(1), 201-213, 2013.
• [11] Ertogan M, Tayyar, GT, Wilson PA, Ertugrul S. “Marine measurement and real-time control systems with case studies”. Ocean Engineering, 159, 457-469, 2018.
• [12] Parikh D, Vohra S, Kaveshgar M. “Comparison of attitude estimation algorithms with IMU under external acceleration”. IEEE International Symposium on Smart Electronic Systems, Formerly iNiS, Jaipur, India, 18-22, December 2021.
• [13] Lei X, Wang R, Fu F. “An adaptive method of attitude and position estimation during GPS outages”. Measurement, 199, 1-11, 2022
• [14] Wang W, Geng Y, Wang K, Si J, Fiaux JDO. “Dynamic toolface estimation for rotary steerable drilling system”. Sensors, 18(9), 2944, 1-17, 2018.
• [15] Benziane L, El Hadri A, Seba A, Benallegue A, Chitour Y. “Attitude estimation and control using linearlike complementary filters: theory and experiment”. IEEE Transactions on Control Systems Technology, 24(6), 2133-2140, 2016.
• [16] Ahmed H, Tahir M. “Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors”. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1723-1739 2016.
• [17] Narkhede P, Joseph Raj AN, Kumar V, Karar V, Poddar S. ”Least square estimation-based adaptive complimentary filter for attitude estimation”. Transactions of the Institute of Measurement and Control, 41(1), 235-245, 2019.
• [18] Liu, M., Cai, Y., Zhang, L., Wang, Y. “Attitude estimation algorithm of portable mobile robot based on complementary filter”. Micromachines, 12(11), 1-13, 2021.
• [19] Meng L, Li B, Childs C, Buis A, He F, Ming D. “Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement”. IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Tianjin, China, 14-16 June 2019.
• [20] Ko NY, Youn W, Choi IH, Song G, Kim TS. “Features of invariant extended Kalman filter applied to unmanned aerial vehicle navigation”. Sensors, 18(9), 1-25, 2018.
• [21] Zhou Q, Li Z, Yu G, Li H, Zhang N. “A novel adaptive Kalman filter for Euler-Angle-Based MEMS IMU/magnetometer attitude estimation”. Measurement Science and Technology, 32(4), 1-17, 2021.
• [22] Farahan SB, Machado JJ, de Almeida FG, Tavares JMR. “9-DOF IMU-Based attitude and heading estimation using an extended kalman filter with bias consideration”. Sensors, 22(9), 3416, 1-25, 2022.
• [23] Stovner BN, Johansen TA, Fossen TI, Schjølberg I. ”Attitude estimation by multiplicative exogenous Kalman filter”. Automatica, 95, 347-355, 2018.
• [24] Poddar S, Narkhede P, Kumar V, Kumar A. “PSO aided adaptive complementary filter for attitude estimation”. Journal of Intelligent & Robotic Systems, 87(3-4), 531-543, 2017.
• [25] Youn W, Rhudy MB, Cho A, Myung H. “Fuzzy adaptive attitude estimation for a fixed-wing UAV with a virtual SSA sensor during a GPS outage”. IEEE Sensors Journal, 20(3), 1456-1472, 2019.
• [26] Yang Q, Sun L. “A fuzzy complementary Kalman filter based on visual and IMU data for UAV landing”. Optik, 173, 279-291, 2018.
• [27] Peng R, Lu P. “Fast adaptive complementary filter for quadrotor attitude estimation during aggressive maneuvers”. IEEE International Conference on Unmanned Aircraft Systems, Athens, Greece, 15-18 June 2021.
• [28] Duong D. Q, Sun J, Nguyen TP, Luo L. “Attitude estimation by using MEMS IMU with fuzzy tuned complementary filter”. IEEE International Conference on Electronic Information and Communication Technology, Harbin, China, 20-22 August 2016.
• [29] Poddar S, Narkhede P, Kumar V, Kumar A. “PSO aided adaptive complementary filter for attitude estimation”. Journal of Intelligent & Robotic Systems, 87, 531-543, 2017.
• [30] Zhang X, Xiao W “A fuzzy tuned and second estimator of the optimal quaternion complementary filter for human motion measurement with inertial and magnetic sensors”. Sensors, 18(10), 3517, 1-14, 2018.
• [31] Hwang SH, Kim DS. “Hybrid helmet attitude tracking system using adaptive complementary filter”. Measurement, 146, 186-194, 2019.
• [32] Zheng L, Zhan X, Zhang X. “Nonlinear complementary filter for attitude estimation by fusing inertial sensors and a camera”. Sensors, 20(23), 1-19, 2020.
• [33] Zhu Y, Liu J, Yu R, Mu Z, Huang L, Chen J, Chen J. “Attitude solving algorithm and FPGA implementation of four-rotor UAV based on improved mahony complementary filter”. Sensors, 22(17), 1-14, 2022.
• [34] Bao H, Du T, Sun L. “Adaptive attitude determination of bionic polarization integrated navigation system based on reinforcement learning strategy”. Mathematical Foundations of Computing, 6(2), 161-177, 2023.
• [35] Tian Y, Wei H, Tan J. “An adaptive-gain complementary filter for real-time human motion tracking with MARG sensors in free-living environments”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(2), 254-264, 2012.
• [36] Kopecki G, Rogalski T. “Aircraft attitude calculation with the use of aerodynamic flight data as correction signals”. Aerospace Science and Technology, 32(1), 267-273, 2014.
• [37] Li W, Wang J. “Effective adaptive Kalman filter for MEMS-IMU/magnetometers integrated attitude and heading reference systems”. The Journal of Navigation, 66(1), 99-113, 2013.
• [38] No H, Cho A, Kee C. “Attitude estimation method for small UAV under accelerative environment”. GPS Solutions, 19(3), 343-355, 2015.
• [39] Youn W, Gadsden SA. “Combined quaternion-based error state Kalman filtering and smooth variable structure filtering for robust attitude estimation”. IEEE Access, 7, 148989-149004, 2019.
• [40] Yoo TS, Hong SK, Yoon HM, Park S. “Gain-scheduled complementary filter design for a MEMS based attitude and heading reference system”. Sensors, 11(4), 3816-3830, 2011.
• [41] Makni A, Fourati H, Kibangou AY. “Energy-aware adaptive attitude estimation under external acceleration for pedestrian navigation”. IEEE/ASME Transactions On Mechatronics, 21(3), 1366-1375, 2015.
• [42] Janusz W, Czyba R, Niezabitowski M, Grzejszczak T. “Expansion of attitude determination algorithms via complementary filtering”. In Proceedings of the IEEE Mediterranean Conference on Control and Automation Valletta, Malta, 3-6 July 2017.
• [43] Mansoor S, Bhatti UI, Bhatti AI, Ali SMD “Improved attitude determination by compensation of gyroscopic drift by use of accelerometers and magnetometers”. Measurement, 131, 582-589, 2019.
• [44] Wen X, Liu C, Huang Z, Su S, Guo X, Zuo Z, Qu H. “A first-order differ ential data processing method for accuracy improvement of complementary filtering in Micro-UAV attitude estimation”. Sensors, 19(6), 1-16, 2019.
• [45] Hooda DS, Raich V. Fuzzy Logic Models and Fuzzy Control. In An Introduction. 1st ed. Oxford UK. Alpha Science International, 2017.
• [46] Hamam A, Georganas ND. “A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of hapto-audio-visual applications”. IEEE International Workshop on Haptic-Audio-Visual Environments and Games, Ottawa, ON, Canada. 18-19 October 2008.
• [47] Kaur A, Kaur A. “Comparison of mamdani-type and sugeno-type fuzzy inference systems for air conditioning system”. International Journal of Soft Computing and Engineering, 2(2), 323-325, 2012.