Kendinden Ayarlamalı PID Denetleyici ile İki Eksenli Gimbal Sisteminin Stabilizasyonu

Yıl 2024, , 1441 - 1452, 25.09.2024

Murat Şahin

https://doi.org/10.2339/politeknik.1210906

Cited By: 2

Öz

Gimbal, füzelerde arayıcının hedef üzerinde kilitlenmesini ve takibini sağlayan, iki eksende hareket kabiliyeti ile görüş açısını arttıran bir sistemdir. Bu çalışmada, füzede kullanılan iki eksenli bir gimbal sisteminin stabilizasyonu gerçekleştirilmiştir. Gimbal stabilizasyonunda dengesizlik, çapraz kuplaj ve ölçülemeyen bozucu etkenler nedeniyle klasik denetleyiciler yerine uyarlamalı denetleyiciler tercih edilmektedir. Stabilizasyon algoritmasındaki eksen kontrolleri için Bulanık Mantık tabanlı Kendinden Ayarlamalı PID denetleyici geliştirilmiştir. Her adımda en uygun katsayının seçilmesi prensibi ile çalışan bu denetleyici sayesinde, uçuş simülatörü ile yapılan testlerde %3'ten daha az hata ile stabilizasyon gerçekleştirmek mümkün olmuştur. Ayrıca karşılaştırma amacıyla Parçacık Sürü Optimizasyonu ile katsayıları ayarlanmış bir PID denetleyici tasarlanmıştır. Deneysel çalışmalarda katsayıları kendinden ayarlanabilir PID denetleyicinin, sabit PID denetleyiciye göre daha iyi sonuç verdiği görülmüştür.

Anahtar Kelimeler

Gimbal, bulanık mantık, kendinden ayarlamalı denetleyici, PID, parçacık sürüsü optimizasyonu

Destekleyen Kurum

Roketsan A. Ş.

Stabilization of Two Axis Gimbal System with Self Tuning PID Control

Öz

Gimbal is a system that provides locking and tracking of the seeker on the target in missiles and increases the angle of view with its mobility in two axes. In this study, stabilization of a two-axis gimbal system used in the missile was carried out. In gimbal stabilization, adaptive controllers are preferred instead of classical controllers due to unbalance, cross-coupling, and unmeasurable disturbances. A Self Tuning PID controller based on Fuzzy Logic was developed for axis controls in the stabilization algorithm. Thanks to this controller, which works with the principle of choosing the most appropriate coefficient at every step, it was possible to control with less than 3% errors in the tests performed with the flight simulator. In addition, a PID controller whose coefficients are optimized with Particle Swarm Optimization is designed for comparison purposes. In experimental studies, it was seen that PID with adjustable coefficients gave better results than the fixed PID.

Anahtar Kelimeler

fuzzy logic, self-tuning controller, particle swarm optimization, Gimbal, PID

Kaynakça

• [1] Ahmed, F., Mohanta, J. C., Keshari, A., and Yadav, P. S., “Recent Advances in Unmanned Aerial Vehicles: A Review”, Arabian Journal for Science and Engineering, 47(7): 7963-7984, (2022).
• [2] Toloei, A. R., Pirzadeh, M., and Vali, A. R., “Design of predictive control and evaluate the effects of flight dynamics on performance of one axis gimbal system, considering disturbance torques”, Aerospace Science and Technology, 54: 143-150, (2016).
• [3] Cong Danh, N., “The Stability of a Two-Axis Gimbal System for the Camera”, The Scientific World Journal, 1-8, (2021).
• [4] Khayatian, M., and Arefi, M. M., “Adaptive dynamic surface control of a two‐axis gimbal system”, IET Science, Measurement & Technology, 10(6): 607-613, (2016).
• [5] Lee, D. H., Tran, D. Q., Kim, Y. B., and Chakir, S., “A robust double active control system design for disturbance suppression of a two-axis gimbal system”, Electronics, 9(10): 1638, (2020).
• [6] Yang, H., Zhao, Y., Li, M., and Wu, F., “The static unbalance analysis and its measurement system for gimbals axes of an inertial stabilization platform”, Metrology and measurement systems, 22(1): 51-68, (2015).
• [7] Wen, T., Xiang, B., and Wong, W., “Coupling analysis and cross-feedback control of three-axis inertially stabilized platform with an active magnetic bearing system”, Shock and Vibration, 1-17, (2020).
• [8] Tong, W., Xiang, B., and Wong, W., “Gimbal torque and coupling torque of six degrees of freedom magnetically suspended yaw gimbal”, International Journal of Mechanical Sciences, 168, 105312, (2020).
• [9] Baskin, M., and Leblebicioğlu, M. K., “Robust control for line-of-sight stabilization of a two-axis gimbal system”, Turkish Journal of Electrical Engineering and Computer Sciences, 25(5): 3839-3853, (2017).
• [10] Hasturk, O., Erkmen, A. M., and Erkmen, I., “Proxy-based sliding mode stabilization of a two-axis gimbaled platform”, Target, 3(4): 1-7, (2011).
• [11] Naderolasli, A., and Tabatabaei, M., “Stabilization of the two-axis gimbal system based on an adaptive fractional-order sliding-mode controller”, IETE Journal of Research, 63(1): 124-133, (2017).
• [12] Mousavi, Y., Zarei, A., and Jahromi, Z. S., “Robust adaptive fractional-order nonsingular terminal sliding mode stabilization of three-axis gimbal platforms”, ISA transactions, 123: 98-109, (2022).
• [13] Mao, J., Li, S., Li, Q., and Yang, J., “Design and implementation of continuous finite-time sliding mode control for 2-DOF inertially stabilized platform subject to multiple disturbances”, ISA transactions, 84: 214-224, (2019).
• [14] Sasaki, T., Shimomura, T., Pullen, S., and Schaub, H., “Attitude and vibration control with double-gimbal variable-speed control moment gyros”, Acta Astronautica, 152: 740-751, (2018).
• [15] Ashok Kumar, M., and Kanthalakshmi, S., “H∞ Control law for line of sight stabilization in two-axis gimbal system”, Journal of Vibration and Control, 28(1-2): 182-191, (2022).
• [16] Altan, A., and Hacıoğlu, R., “Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances”, Mechanical Systems and Signal Processing, 138, 106548, (2020).
• [17] Jahanandish, R., Khosravifard, A., and Vatankhah, R., “Determination of uncertain parameters of a two-axis gimbal and motion tracking via Fuzzy logic control approach”, Journal of Intelligent & Fuzzy Systems, 39(5): 6565-6577, (2020).
• [18] Abdo, M. M., Vali, A. R., Toloei, A. R., and Arvan, M. R., “Stabilization loop of a two axes gimbal system using self-tuning PID type fuzzy controller”, ISA transactions, 53(2), 591-602, (2014).
• [19] Danh, N. C., “LQG, PID controller, ANN for single axis gimbal actuator”, EAI Endorsed Transactions on AI and Robotics, 1(1): 1-11, (2022).
• [20] Sharma, J., Hote, Y. V., and Prasad, R., “Robust PID control of single-axis gimbal actuator via stability boundary locus”, IFAC-PapersOnLine, 53(1): 27-32, (2020).
• [21] Senthil Kumar, S., and Anitha, G., “Fuzzy Logic-Based Self-Tuning PID Controllers Using Parameters Adaptive Method for Stabilization of a Two-Axis Seeker Gimbal”, IETE Journal of Research, 1-10, (2021).
• [22] Srivastava, A.K., Kumar, D., Tripathi, S.M. and Sen, P.K., “Comparative study of proportional-integral and proportional-integral-derivative (PI and PID) controllers for Z-source inverter-fed induction motor drive”, 2nd International Conference on Power, Control and Embedded Systems (ICPCES), Allahabad, India, 1-6, (2012).
• [23] Sahin, M., “Kendinden ayarlamalı PID kontrolör ile iki eksenli gimbal uygulaması”, PhD Thesis, Gazi University, Graduate School of Natural and Applied Sciences, (2015).
• [24] Hemingway, E. G., and O’Reilly, O. M., “Perspectives on Euler angle singularities, gimbal lock, and the orthogonality of applied forces and applied moments”, Multibody System Dynamics, 44(1): 31-56, (2018).
• [25] Senthil Kumar, S., and Anitha, G., “A novel self-tuning fuzzy logic-based PID controllers for two-axis gimbal stabilization in a missile seeker”, International Journal of Aerospace Engineering, 1-12, (2021).
• [26] El-Samahy, A. A., and Shamseldin, M. A., “Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control”, Ain Shams Engineering Journal, 9(3): 341-352, (2018).
• [27] Aissaoui, A. G., and Tahour, A., “Application of Fuzzy Logic in Control of Electrical Machines”, Fuzzy Logic – Controls, Concepts, Theories and Applications, 107-129, (2012).
• [28] Tüzüner, Z., and Bal, H., “Three Group Classification Problem Approach Based on Fuzzy Goal Programming”, Politeknik Dergisi, 23(4): 1089-1095, (2020).
• [29] Xue, P., Wang, H., Hou, J., and Li, W., “Based on the Fuzzy PID Brushless DC Motor Control System Design”, International Conference on Measurement, Information and Control, 703-706, (2012).
• [30] Pedrammehr, S., Qazani, M. R. C., Asadi, H., Ettefagh, M. M., and Nahavandi, S., “Model-based control of axisymmetric hexarot parallel manipulators”, Results in Control and Optimization, 7, 100135, (2022).
• [31] Pourjavad, E., and Mayorga, R. V., “A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system”, Journal of Intelligent Manufacturing, 30(3): 1085-1097, (2019).
• [32] Dereli, S., Köker, R., Öylek, İ., and Mükremin, A. Y., “A comprehensive research on the use of swarm algorithms in the inverse kinematics solution”, Politeknik Dergisi, 22(1): 75-79, (2019).
• [33] Tefek, M. F., “Küresel Optimizasyon Problemlerinin Çözümü İçin Zamanla Değişen Rastgele Atalet Ağırlıklı Jaya Algoritması”, Politeknik Dergisi, 25(1): 123-135, (2022).
• [34] Kaveh, A., “Particle swarm optimization”, In Advances in Metaheuristic Algorithms for Optimal Design of Structures, 11-43, (2017).