Volume 18, no. 3Pages 5 - 15

Method of Dynamic Synthesis of Adaptive LQR Controller Coefficients in Combination with PID Controller in the Control System of a Multirotor UAV

D.M. Volkov, A.I. Saveliev
The objective of this work is to improve the robustness and stability of the control system of a multirotor unmanned aerial vehicle (CS UAV) using a new method of dynamic LQR synthesis of gain factors and generation of control actions. The developed method ensures real-time adaptation of the UAV CS to external influences due to dynamically changing weighting factors of the cost matrices Q and R, as well as generated coefficients of the UAV dynamic model. A comparison of the synthesized controller based on the developed method with a PID controller, which is widely used in modern UAVs, is carried out. It is revealed that the developed method works comparatively better, showing, under certain experimental conditions, the percentage of correlation of the output action to the desired angular position is 26 % greater than the PID controller with static coefficients. At the same time, the time interval of the transient regulation process using the proposed method is on average 5 times smaller than that of the considered analogue.
Full text
Keywords
LQR; LQ-regulator; adaptive LQR; adaptive regulator; adaptive LQ- regulator; CS UAV.
References
1. Praveen V., Pillai S. Modeling and Simulation of Quadcopter using PID Controller. International Journal Control Theory and Applications, 2016, vol. 9, no. 15, pp. 7151-7158.
2. Hayes B., Condon M., Giaouris D. Design of PID Controllers using Filippov's Method for Stable Operation of DC-DC Converters. International Journal Circuit Theory Applications, 2016, vol. 44, no. 7, pp. 1437-1454. DOI: 10.1002/cta.2169
3. Xu Jinqiang. An Expert PID Control Algorithm Based on Anti-Integration Saturation. Proceedings 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, 2017, pp. 1536-1539. DOI: 10.1109/IAEAC.2017.8054270
4. Chen Wen-Hua, Ballance D.J., Gawthrop P.J., Gribble J.J., O'Reilly J. Nonlinear PID Predictive Controller. IEE Proceedings Control Control Theory and Applications, 1999, vol. 146, no. 6, pp. 603-611. DOI: 10.1049/ip-cta:19990744
5. Simon J.D., Mitter S.K. A Theory of Modal Control. Information and Control, 1968, vol. 13, no. 4, pp. 316-353. DOI: 10.1016/S0019-9958(68)90834-6
6. Kositsyn V.G., Soloviev V.A. Synthesis of Control Systems with a Fuzzy Modal Controller. Computer Science and Control Systems, 2002, no. 2, pp. 82-87.
7. Kickert W.J., Mamdani E.H. Analysis of a Fuzzy Logic Controller. In Readings in Fuzzy Sets for Intelligent Systems, 1993, vol. 1, no. 1, pp. 290-297.
8. Chicherova E.V. Study of Robustness Properties of Fuzzy Logic Controllers Using the Example of a Gas Turbine Engine Fuel Consumption Control Loop. Bulletin of the Samara State Aerospace University Named after Academician S.P. Korolev, 2012, no. 3-1 (34), pp. 145-152.
9. Filimonov A.B., Filimonov N.B. Some Problematic Aspects of Fuzzy PID Control. Mechatronics, Automation, Control, 2018, vol. 19, no. 12, pp. 762-769. DOI: 10.17587/mau/19.762-769
10. Kibardin V.V., Kovaleva O.A., Yazev V.N. Optimal Control Criteria and LQR Optimization in an Electric Drive. Bulletin of the Krasnoyarsk State Agrarian University, 2015, no. 12, pp. 61-73.
11. Argentim L.M., Rezende W.C., Santos P.E., Aguiar R.A. PID, LQR and LQR-PID on a Quadcopter Platform. Proceedings 2013 - International Conference Informatics, Electronics and Vision, Dhaka, 2013, pp. 1-6. DOI: 10.1109/ICIEV.2013.6572698
12. Shilin A., Pham Trong H., Nguyen Vuong V. Synthesis of a Fuzzy Controller by a Second-Order Object with Delay. Informatics and Automation, 2024, no. 5 (23), pp. 1505-1531. DOI: 10.15622/ia.23.5.9
13. Shmalko E., Serebrenny V. Optimal Control Problems in Collaborative Multi-Agent Robotic Systems. Interactive Collaborative Robotics, 2024, vol. 14898, pp. 281-292. DOI: 10.15622/ia.23.5.9
14. Tuyen T., Tinh T., Krestovnikov K. Adaptive Fast Terminal Sliding Mode (FTSM) Control and High Gain Observer (HGO) for Multi-Motor Web Transport Systems. Interactive Collaborative Robotics, 2024, vol. 14898, pp. 131-143. DOI: 10.1007/978-3-031-71360-6_1
15. Kolotov M.E., Smirnova T.A. Decomposition of the Linear Model of a Quadcopter. Young Scientist, 2016, no. 13, pp. 29-33.
16. Luukkonen, T. Modelling and Control of Quadcopter. Independent Research Project in Applied Mathematics, Espoo, 2011, vol. 22, no. 22, 26 p.
17. Dharmawan A., Priyambodo T.K. Model of Linear Quadratic Regulator (LQR) Control Method in Hovering State of Quadrotor. Journal of Telecommunication, Electronic and Computer Engineering, 2017, vol. 9, no. 3, pp. 135-143.