Current research on quadrotor modeling mainly focuses on theoretical analysis methods and experimental methods, which have problems such as weak adaptability to the environment, high test costs, and long durations. Additionally, the PID controller, which is currently widely used in quadrotors, requires improvement in anti-interference. Therefore, the aforementioned research has considerable practical significance for the modeling and controller design of quadrotors with strong coupling and nonlinear characteristics. In the present research, an aerodynamic-parameter estimation method and an adaptive attitude control method based on the linear active disturbance rejection controller (LADRC) are designed separately. First, the motion model, dynamics model, and control allocation model of the quad-rotor are established according to the aerodynamic theory and Newton–Euler equations. Next, a more accurate attitude model of the quad-rotor is obtained by using a tool called CIFER to identify the aerodynamic parameters with large uncertainties in the frequency domain. Then, an adaptive attitude decoupling controller based on the LADRC is designed to solve the problem of the poor anti-interference ability of the quad-rotor and adjust the key control parameter b0 automatically according to the change in the moment of inertia in real time. Finally, the proposed approach is verified on a semi-physical simulation platform, and it increases the tracking speed and accuracy of the controller, as well as the anti-disturbance performance and robustness of the control system. This paper proposes an effective aerodynamic-parameter identification method using CIFER and an adaptive attitude decoupling controller with a sufficient anti-interference ability.
Sen Yang
,
Leiping Xi
,
Jiaxing Hao
,
Wenjie Wang
. Aerodynamic-Parameter Identification and Attitude Control of Quad-Rotor Model with CIFER and Adaptive LADRC[J]. Chinese Journal of Mechanical Engineering, 2021
, 34(2)
: 1
-1
.
DOI: 10.1186/s10033-020-00524-5
Current research on quadrotor modeling mainly focuses on theoretical analysis methods and experimental methods, which have problems such as weak adaptability to the environment, high test costs, and long durations. Additionally, the PID controller, which is currently widely used in quadrotors, requires improvement in anti-interference. Therefore, the aforementioned research has considerable practical significance for the modeling and controller design of quadrotors with strong coupling and nonlinear characteristics. In the present research, an aerodynamic-parameter estimation method and an adaptive attitude control method based on the linear active disturbance rejection controller (LADRC) are designed separately. First, the motion model, dynamics model, and control allocation model of the quad-rotor are established according to the aerodynamic theory and Newton–Euler equations. Next, a more accurate attitude model of the quad-rotor is obtained by using a tool called CIFER to identify the aerodynamic parameters with large uncertainties in the frequency domain. Then, an adaptive attitude decoupling controller based on the LADRC is designed to solve the problem of the poor anti-interference ability of the quad-rotor and adjust the key control parameter b0 automatically according to the change in the moment of inertia in real time. Finally, the proposed approach is verified on a semi-physical simulation platform, and it increases the tracking speed and accuracy of the controller, as well as the anti-disturbance performance and robustness of the control system. This paper proposes an effective aerodynamic-parameter identification method using CIFER and an adaptive attitude decoupling controller with a sufficient anti-interference ability.
[1] Róbert Szabolcsi. The quadrotor-based night watchbird UAV system used in the force protection tasks. International Conference Knowledge-Based Organization, 2015, 21(3): 170-184.
[2] Omid Mofid, Saleh Mobayen. Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties. ISA Transactions, 2018.
[3] Lebsework Negash, Sang-Hyeon Kim, Han-Lim Choi. An eigenstructure assignment embedded unknown input observe approach for actuator fault detection in quadrotor dynamics. IFAC Papers OnLine, 2016, 49(17): 426-431.
[4] Yousaeng Lee, Seungjoo Kim, Jinyong Suk, et al. System identification of an unmanned aerial vehicle from automated fight tests. AIAA-2002-2003, 2002.
[5] Hanbing Li, Dawei Wu. An approach of UAV's aerodynamic parameter identification. Flight Dynamics, 2014, 32(2): 183-188.
[6] Congkui Hao. The attitude control system and control method of quad-rotor. North China University of Technology, 2014.
[7] Juping Wang. Research on parameter identification of permanent magnet synchronous motor based on neural network. Shanghai: Donghua University, 2016.
[8] Yu Zou, Hailong Pei, Xin Liu. Study on CIFER algorithm, a method for frequency identification of aircraft model. Electronic Optics & Control, 2010, 17(5): 46-49.
[9] Jing-Jing Xiong, Guo-Bao Zhang. Global fast dynamic terminal sliding mode control for a quadrotor UAV. ISA Transactions, 2017, 66: 233-240.
[10] S Gilbert, E Varghese. Design and simulation of robust filter for tracking control of quadcopter system. 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2017.
[11] S K Kim, C K Ahn. Auto-tuner based controller for quadcopter attitude tracking applications. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2019, 66(12): 2012-2016.
[12] H Wang, X Ye, Y Tian, et al. Attitude control of a quadrotor using model free based sliding model controller. International Conference on Control Systems and Computer Science, IEEE, 2015: 149-154.
[13] W C Lee, H L Choi. Interactive multiple neural adaptive observer based sensor and actuator fault detection and isolation for quadcopter. 2019 International Conference on Unmanned Aircraft Systems (ICUAS), 2019.
[14] Chun Kiat Tan, Jianliang Wang, Yew Chai Paw, et al. Tracking of a moving ground target by a quadrotor using a backstepping approach based on a full state cascaded dynamics. Applied Soft Computing, 2016: 47-62.
[15] H Liu, X Wang, Y Zhong. Quaternion-based robust attitude control for quadrotors. IEEE Transactions on Industrial Informatics, 2015, 11(2): 406-415.
[16] Jingqing Han. Auto disturbances rejection controller and its applications. Control and Decision, 1998, 13(1): 19-23.
[17] X Liang, J Li, F Zhao. Attitude control of quadrotor UAV based on LADRC method. 2019 Chinese Control and Decision Conference (CCDC), 2019.
[18] Jie Li, Xiaohui Qi, Yuanqing Xia, et al. On asymptotic stability for nonlinear ADRC based control system with application to the ball-beam problem. Proceedings of the American Control Conference, 2016.
[19] Wenchao Xue, Yi Huang. Tuning of sampled-data ADRC for nonlinear uncertain systems. Journal of Systems Science & Complexity, 2016: 1-25.
[20] Zhiqiang Gao. Scaling and bandwidth-parameterization based controller tuning. Proceedings of the American Control Conference, Denver, Colorado, June4-6, 2003: 4989-4996.
[21] S Zhao, Z Gao. Active disturbance rejection control for non-minimum phase systems. Proceedings of the 29th Chinese Control Conference, Beijing, 2010: 6060-6070.
[22] Xing Chen. Active disturbance rejection controller tuning and its applications to thermal processes. Beijing: Tsinghua University, 2008.
[23] S Li, Z Liu. Adaptive speed control for permanent-magnet synchronous motor system with various of load inertia. IEEE Transactions on Industrial Electronics, 2009, 56(8): 3050-3059.
[24] A Morfin-Santana, F M Palacios, I Gonzalez-Hernandez, et al. Robust control for octorotor Unmanned Aerial Vehicle in H-Configuration. 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2018.
[25] M H Jafri, H Mansor, T S Gunawan. Development of fuzzy logic controller for quanser bench-top helicopter. Materials Science and Engineering, 2017: 2-8.
[26] Yu Zou. Frequency identification of UAV based on CIFER. Guangzhou: South China University of Technology, 2015.
[27] M S Rafaq, J W Jung. A comprehensive review of state-of-the-art parameter estimation techniques for permanent magnet synchronous motors in wide speed range. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4747-4758.
[28] B Tian, L Liu, H Lu, et al. Multivariable finite time attitude control for quadrotor UAV: Theory and experimentation. IEEE Transactions on Industrial Electronics, 2017: 1-1.
[29] A Federico, Z Emanuele, M Ammar, et al. Knot-tying with flying machines for aerial construction. IEEE RSJ International Conference on Intelligent Robots and Systems, 2015: 5917-5922.
[30] W Mizouri, S Najar, M Aoun, et al. Modeling and control of a quadrotor UAV. Proceedings of 15th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering, 2014: 343-348.
[31] J Zhou, X Lyu, X Cai, et al. Frequency domain model identification and loop-shaping controller design for quadrotor tail-sitter VTOL UAVs. ICUAS, 2018: 1142-1149.
[32] X Lyu, H Gu, J Zhou, et al. Simulation and flight experiments of a quadrotor tail-sitter vertical take-off and landing unmanned aerial vehicle with wide flight envelope. International Journal of Micro Air Vehicles, 2018, 10(4): 303-317.
