簡易檢索 / 詳目顯示

研究生: 李承益
Cheng-YI Li
論文名稱: 以具抗風性的干擾觀測器有限時間跟隨控制於多台無GPS導航之無人機
Disturbance-Observer-Based Finite-Time Following Control with Wind Resistance Capability for Multiple GPS-Denied UAVs
指導教授: 黃志良
Chih-Lyang Hwang
口試委員: 黃志良
陳永耀
連豊力
林柏廷
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 41
中文關鍵詞: 干擾觀測器有限時間跟隨控制多無人機李雅普諾夫穩定性理論底層PID跟隨控制
外文關鍵詞: Disturbance observer, Finite-time following control, Multiple UAVs, Lyapunov stability theory, Low-level PID following control
相關次數: 點閱:45下載:2
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 在本論文中,我們設計一種基於具有抗風阻能力的干擾觀測器之有限時間跟隨控制(DO-FTFC-WRC),以快速實現在無GPS訊號的區域,例如倉庫、圖書館中,對多架無人機進行指定跟隨任務。每架無人機中的底層PID跟隨控制(LL-PID-FC)視為一個內部控制迴路。相反,它的外部迴路由一個具有未知干擾的二階系統模式,包括系統不確定性、感知器雜訊或額外的風勢。將領隊跟隨想要的三維姿態設為虛擬領隊。接著,隨後的追隨者逐一執行所設想的隊形。為了在不確定的環境中完成任務,所設計的DO-FTFC-WRC具有耦合的跟隨誤差、非線性切換增益,及干擾觀測器以估測每架無人機中的動態不確定性。不僅非線性切換增益隨著耦合的跟隨誤差接近零而增加,以完成其快速有限時間追蹤能力,同時以干擾觀測器即時補償不確定性。最後,通過模擬和實驗,與LL-PID-FC相比,考慮或不考慮更多的風條件,以代表性的“向前-順時針180度-往後運動”軌跡,驗證所提方法的優越性。

    關鍵詞:干擾觀測器,有限時間跟隨控制,多無人機,李雅普諾夫穩定性理論,底層PID跟隨控制。


    Abstract---In this thesis, a disturbance-observer-based finite-time following control wind resistance capability (DO-FTFC-WRC) is designed to quickly accomplish an assigned following of multiple UAVs in a GPS-denied region, e.g., warehouse, library. The low-level PID following control (LL-PID-FC) in each UAV is an inner control loop. In contrast, its outer loop is modeled by a second-order system with unknown disturbance, including system uncertainties, sensor noises or extra wing gust. The virtual leader is modeled as the desired 3D pose for the leader to follow. Then the consequent followers are one-by-one to execute the desired formation. To fulfill the task under the uncertain environment, the proposed DO-FTFC-WRC possesses coupled following error, nonlinear switching gain, and disturbance observer for estimating dynamic uncertainties in each UAV. Not only does the nonlinear switching gains increase as the coupled following error is in the vicinity of zero to achieve its fast finite-time convergence, but also the uncertainties are online compensated by disturbance observer. Finally, the simulation and experiment in comparison to LL-PID-FC with or without wind condition for a representative “forward- clockwise turning-backward motion” are presented to validate the superiority of the proposed approach.

    Key words: Disturbance observer, Finite-time following control, Multiple UAVs, Lyapunov stability theory, Low-level PID following control.

    摘 要 i Abstract ii 圖目錄 v 表目錄 vii 第一章 論文與文獻回顧 1 第二章 問題表述 3 第三章 具抗風性的干擾觀測器之有限時間跟隨控制 8 3.1 預備數學 8 3.2 DO-FTFC-WRC的設計 8 3.3 DO-FTFC-WRC演算法 11 3.4 穩定性分析 11 第四章 模擬與討論 16 第五章 實驗結果與討論 20 5.1系統構成 20 5.1.1 機架 21 5.1.2 飛行控制器 22 5.1.3 直流無刷馬達 23 5.1.4 電子速度控制器 24 5.1.5 螺旋槳 24 5.1.6 px4光流 25 5.1.7 Jetson Xavier NX 25 5.1.8 測距儀 26 5.1.9 電池 27 5.1.10 遙控器 27 5.2 擴展卡爾曼濾波器(EKF)導航概述 28 5.3 實驗環境描述 29 5.4 實驗結果 29 第六章 結論 37 參考文獻 38

    [1] D. Liu, H. Liu, F. L. Lewis, and Y. Wan, “Robust fault-tolerant formation control for tail-sitters in aggressive flight mode transitions,” IEEE Trans. Ind. Inform., vol. 16, no. 1, pp. 299-308, Jan. 2020.
    [2] D. N. Das, R. Sewani, J. Wang, and M. K. Tiwari, “Synchronized truck and drone routing in package delivery logistics,” IEEE Trans. Intell. Transport. Syst., vol. 22, no. 9, pp. 5772-5782, Sep. 2021.
    [3] X. Li, J. Wei, and H. Jiao, “Real-time tracking algorithm for aerial vehicles using improved convolutional neural network and transfer learning,” IEEE Trans. Intell. Transport. Syst., vol. 23, no. 3, pp. 2296-2305, Mar. 2022.
    [4] M. Samir, S. Sharafeddine, C. M. Assi, T. M. Nguyen, and A. Ghrayeb, “UAV trajectory planning for data collection from time-constrained IoT devices,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 34-46, Jan. 2020.
    [5] D. Liu, W. Bao, X. Zhu, B. Fei, T. Men, and Z. Xiao, “Cooperative path optimization for multiple UAVs surveillance in uncertain environment,” IEEE Internet Things J., vol. 9, no. 13, pp. 10676-10692, Jul. 2022.
    [6] K. Guo, C. Liu, X. Zhang, X. Yu, Y. Zhang, L. Xie, and Lei Guo, “A bio-inspired safety control system for UAVs in confined environment with disturbance,” IEEE Trans. Cybern., DIO: 10.1109/TCYB.2022. 3217982.
    [7] Y. Zhou, H. He, and C. Sun, "Fully distributed finite-time consensus of directed multiquadcopter systems via pinning control," IEEE Trans. Sys., Man, Cybern.: Syst., vol. 51, no. 8, pp. 5080-5089, Aug. 2021.
    [8] B. S. Chen, C. P. Wang, and M. Y. Lee, "Stochastic robust team tracking control of multi-UAV networked system under Wiener and Poisson random fluctuations," IEEE Trans. Cybern., vol. 51, no. 12, pp. 5786-5799, Dec. 2021.
    [9] Y. Zou, H. Zhang, and W. He, "Adaptive coordinated formation control of heterogeneous vertical takeoff and landing UAVs subject to parametric uncertainties," IEEE Trans. Cybern., vol. 52, no. 5, pp. 3184-3195, May 2022.
    [10] G. Feng, D. Dang, and Y. He, “Robust coordinated control of nonlinear heterogeneous platoon interacted by uncertain topology,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 4982-4992, Jun. 2022.
    [11] Z. Zhang, L. Zheng, Y. Zhou, and Q. Guo, “A novel finite-time-gain-adjustment controller design method for UAVs tracking time-varying targets,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 12531-12543, Aug. 2022.
    [12] J. Wang, X. Luo, J. Yan, and X. Guan, “Distributed integrated sliding mode control for vehicle platoons based on disturbance observer and multi power reaching law,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 4, pp. 3366-3376, Apr., 2022.
    [13] C.-L. Hwang and H. B. Abebe, “Generalized and heterogeneous nonlinear dynamic multiagent systems using online RNN-based finite-time formation tracking control and application to transportation systems,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 13708-13720, Aug. 2022.
    [14] B. Ning, Q.-L. Han, Q. Lu, and J. Sanjayan, “Cooperative control of multi-robot systems subject to control gain uncertainty,” IEEE Trans. Ind. Inform., vol. 19, no. 4, pp. 5367-5376, Apr. 2023.
    [15] T. Yin, Z. Gu, and X. Xie, “Observer-based event-triggered sliding mode control for secure formation tracking of multi-UAV systems,” IEEE Trans. Net. Sci. Eng., vol. 10, no. 2, pp. 887-898, Mar/Apr. 2023.
    [16] G. Raja, S. Essaky, A. Ganapathisubramaniyan, and Y. Baskar, “Nexus of deep reinforcement learning and leader –follower approach for AIoT enabled aerial networks,” IEEE Trans. Ind. Inform., vol. 19, no. 8, pp. 9165-9172, Aug. 2023.
    [17] Y. Yu, C. Chen, J. Guo, M. Chadli, and Z. Xiang, “Adaptive formation control for unmanned aerial vehicles with collision avoidance and switching communication network,” IEEE Trans. Fuzzy Syst., DOI 10.1109/TFUZZ.2023.3327114.
    [18] Y. Yang, S. Gorbachev, B. Zhao, Q. Liu, Z. Shu, and D. Yue, “Predictor-based neural attitude control of a quadrotor with disturbances,” IEEE Trans. Ind. Inform., vol. 20, no. 1, pp. 169-177, Jan. 2024.
    [19] L. Qiao and W. Zhang, “Trajectory tracking control of AUVs via adaptive fast nonsingular integral terminal sliding mode control,” IEEE Trans. Ind. Inform., vol. 16, no. 2, pp. 1248-1258, Feb. 2020.
    [20] Z. Liang, Z. Wang, J. Zhao, and X. Ma, “Fast finite-time path-following control for autonomous vehicle via complete model-free approach,” IEEE Trans. Ind. Inform., vol. 19, no. 3, pp. 2838-2846, Mar. 2023.
    [21] C.-L. Hwang, T.-Y. Wu, and S.-E. Pu, “Specific human following by residual-Bi-LSTM-based distributed module UWB network and residual-RNN-based finite-time control,” IEEE Trans. Ind. Inform., DOI: 0.1109/TII.2023.3313640.
    [22] I. Lopez-Sanchez, J. Moyrón, J. Moreno-Valenzuela, “Adaptive neural network-based trajectory tracking outer loop control for a quadrotor,” Aerospace Sci. Technol., vol. 129, p 107847, 2021.
    [23] C.-L. Hwang, C. C. Chiang, and Y. W. Yeh, “Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain under-actuated nonlinear dynamic systems,” IEEE Trans. Fuzzy Syst., vol. 22, no. 2, pp. 286-297, Apr. 2014.
    [24] S. Yun, Y. J. Lee and S. Sung, “IMU vision Lidar integrated navigation system in GNSS denied environments,” 2013 IEEE Aerospace Conference, Big Sky, MT, USA, pp. 1-10, 02-09 March 2013.
    [25] PX4FLOW, https://docs.px4.io/v1.11/zh/sensor/px4flow.html.
    [26] C.-L. Hwang, J. Y. Lai, and Z. S. Lin, “Sensor-fused fuzzy variable structure incremental control for partially known nonlinear dynamic systems and application to an outdoor quadrotor,” IEEE/ASME Trans. Mechatronics, vol. 25, no. 2, pp. 716-727, Apr. 2020.

    QR CODE