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RETRACTED ARTICLE: Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot

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This article was retracted on 08 April 2024

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Abstract

Power transmission lines require efficient and reliable tree pruning to maintain their operation. This paper presents an adaptive Alternating Direction Method of Multipliers (ADMM)-based fast Model Predictive Control (MPC) for aerial tree pruning robots to address low operating efficiency and high labor costs. The proposed control strategy leverages MPC, a modern control method proven effective in complex systems, including aerial robots, to handle the challenges of attitude and position control during tree pruning operations. The adaptive ADMM algorithm is employed to solve constrained Quadratic Programming (QP) problems in real-time, enabling the robot to respond quickly to dynamic changes and maintain stability. Designed to perform real-time calculations on embedded computers with limited computing power, the control strategy is well-suited for implementation on aerial pruning robots. Improved operational capabilities, such as faster job site access, larger working space, and fossil fuel-free operation, result in increased efficiency and reduced labor costs. The paper covers the dynamic model of the pruning robot, the fast MPC control scheme, the adaptive ADMM for solving the QP problem, and the successful simulation and experimental implementation of the proposed control strategy on the aerial pruning robot.

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References

  • Amin R, Aijun L (2017) Design of mixed sensitivity H∞ control for four-rotor hover vehicle. Int J Autom Control 11(1):89–103

    Article  Google Scholar 

  • Ansari U, Bajodah A (2017) Robust generalized dynamic inversion quadrotor control. IFAC-PapersOnLine 50(1):8181–8188

    Article  Google Scholar 

  • Bolognani S et al (2008) Design and implementation of model predictive control for electrical motor drives. IEEE Trans Ind Electron 56(6):1925–1936

    Article  Google Scholar 

  • X Chen, L Wang (2013) Cascaded model predictive control of a quadrotor UAV. In: Australian Control Conference, pp. 354–359

  • G Darivianakis, A Kostas, M Burri, R Siegwart (2014) Hybrid predictive control for aerial robotic physical interaction towards inspection operations. In: IEEE international conference on robotics and automation (ICRA), pp 53–58

  • Dong B, Lam K (2014) A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Build Simul 7(1):89–106

    Article  Google Scholar 

  • Dufour P, Michaud D et al (2004) A partial differential equation model predictive control strategy: application to autoclave composite processing. Comput Chem Eng 28(4):545–556

    Article  Google Scholar 

  • Elkhatem Aisha Sir, Engin Seref Naci (2022) Robust LQR and LQR-PI control strategies based on adaptive weighting matrix selection for a UAV position and attitude tracking control. Alex Eng J 61(8):6275–6292

    Article  Google Scholar 

  • Hafez A, Marasco A, Givigi S, Iskandarani M, Yousefi S, Rabbath C (2015) Solving multi-UAV dynamic encirclement via model predictive control. IEEE Trans Control Syst Technol 23(6):2251–2265

    Article  Google Scholar 

  • Hans C, Braun P, Raisch J, Grüne L, Reincke-Collon C (2018) Hierarchical distributed model predictive control of interconnected microgrids. IEEE Trans Sustain Energy 10(1):407–416

    Article  Google Scholar 

  • Hu C et al (2018) Decentralized real-time estimation and tracking for unknown ground moving target using UAVs. IEEE Access 7:1808–1817

    Article  Google Scholar 

  • Kostas A, Nikolakopoulos G, Tzes A (2011) Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances. Control Eng Pract 19(10):1195–1207

    Article  Google Scholar 

  • Kostas A, Nikolakopoulos G, Tzes A (2012) Model predictive quadrotor control: attitude, altitude and position experimental studies. IET Control Theory Appl 6(12):1812–1827

    Article  MathSciNet  Google Scholar 

  • Kostas A, Papachristos C, Siegwart R, Tzes A (2016) Robust model predictive flight control of unmanned rotorcrafts. J Intell Rob Syst 81(3–4):443–469

    Google Scholar 

  • A Kostas, G Nikolakopoulos, A Tzes (2010a) Constrained optimal attitude control of a quadrotor helicopter subject to wind-gusts: experimental studies. In: Proceedings of the 2010a American Control Conference, pp. 4451–4455

  • A Kostas, G Nikolakopoulos, A Tzes (2010b)”Design and experimental verification of a constrained finite time optimal control scheme for the attitude control of a quadrotor helicopter subject to wind gusts,” In: IEEE International Conference on Robotics and Automation, pp 1636–1641

  • Li R, Chen M, Wu Q (2017) Adaptive neural tracking control for uncertain nonlinear systems with input and output constraints using disturbance observer. Neurocomputing 235:27–37

    Article  Google Scholar 

  • Li B, Zhou W, Sun J, Wen C, Chen C (2018) Development of model predictive controller for a Tail-Sitter VTOL UAV in hover flight. Sensors 18(9):2859–2879

    Article  Google Scholar 

  • Lu K, Du P, Cao J (2019) A novel traffic signal split approach based on explicit model predictive control. Math Comput Simul 155:105–114

    Article  MathSciNet  Google Scholar 

  • Ma D, Xia Y, Li T et al (2016) Active disturbance rejection and predictive control strategy for a quadrotor helicopter. IET Control Theory Appl 10(17):2213–2222

    Article  MathSciNet  Google Scholar 

  • Mahyar A, Zhang Y, Rabbath C (2013) An efficient model predictive control scheme for an unmanned quadrotor helicopter. J Intell Rob Syst 70(1–4):27–38

    Google Scholar 

  • Molina J, Hirai S (2017) Aerial pruning mechanism, initial real environment test. Robot Biomim 4:15

    Article  Google Scholar 

  • Papachristos C, Kostas A, Tzes A (2014) Technical activities execution with a tiltrotor uas employing explicit model predictive control. IFAC Proceedings 47(3):11036–11042

    Google Scholar 

  • Poksawat P, Wang L, Mohamed A (2017) Gain scheduled attitude control of fixed-wing UAV with automatic controller tuning. IEEE Trans Control Syst Technol 26(4):1192–1203

    Article  Google Scholar 

  • Prach A, Kayacan E (2018) An MPC-based position controller for a tilt-rotor tricopter VTOL UAV. Opt Control Appl Methods 39(1):343–356

    Article  Google Scholar 

  • A Schoelling (2012) Improving tracking performance by learning from past data. Ph.D. dissertation, Institute for Dynamic Systems and Control, ETH Zurich, Zurich, Switzerland

  • Sun Qiyu, Fang Jinbao, Zheng Wei Xing, Tang Yang (2022) Aggressive quadrotor flight using curiosity-driven reinforcement learning. IEEE Trans Ind Electron 69(12):13838–13848

    Article  Google Scholar 

  • Timotheou S, Panayiotou C, Polycarpou M (2014) Distributed traffic signal control using the cell transmission model via the alternating direction method of multipliers. IEEE Trans Intell Transp Syst 16(2):919–933

    Google Scholar 

  • B Tomas, D Hert et al.(2018) Model predictive trajectory tracking and collision avoidance for reliable outdoor deployment of unmanned aerial vehicles, In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6753–6760

  • Vazquez S, Rodriguez J, Rivera M, Franquelo L, Norambuena M (2016) Model predictive control for power converters and drives: advances and trends. IEEE Trans Industr Electron 64(2):935–947

    Article  Google Scholar 

  • Wang S, Chen J, He X (2022) An adaptive composite disturbance rejection for attitude control of the agricultural quadrotor UAV. ISA Trans 129:564–579

    Article  Google Scholar 

  • Xiong J, Zhang G (2017) Global fast dynamic terminal sliding mode control for a quadrotor UAV. ISA Trans 66:233–240

    Article  Google Scholar 

  • Yang H, Cheng L, Xia Y, Yuan Y (2017) Active disturbance rejection attitude control for a dual closed-loop quadrotor under gust wind. IEEE Trans Control Syst Technol 26(4):1400–1405

    Article  Google Scholar 

  • M Yavari, K Gupta, M Mehrandezh, A Ramirez-Serrano (2018) Optimal real-time trajectory control of a pitch-hover UAV with a two link manipulator. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 930–938

  • Yeonsik K, Hedrick J (2009) Linear tracking for a fixed-wing UAV using nonlinear model predictive control. IEEE Trans Control Syst Technol 17(5):1202–1210

    Article  Google Scholar 

  • Zhang J (2014) Modeling and constrained multivariable predictive control for ORC (organic rankine cycle) based waste heat energy conversion systems. Energy 66:128–138

    Article  Google Scholar 

  • Zhu H, Javier A (2019) Chance-constrained collision avoidance for MAVs in dynamic environments. IEEE Robot Autom Lett 4(2):776–783

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the Guizhou Provincial Science and Technology Projects, grant number Guizhou-Sci-Co-Supp[2020]2Y044; the Science and Technology Projects of China Southern Power Grid Co. Ltd., grant number 066600KK52170074; the Key Laboratory Projects of Aeronautical Science Foundation of China, grant number 201928052006; the Key Laboratory Projects of Aeronautical Science Foundation of China, grant number 20162852031; the Research Innovation Program for Postgraduates of Universities in Jiangsu Province, grant number KYLX16_0380. University-level scientific research project of Nanjing Xiaozhuang University, grant number 2022NXY23.

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Authors and Affiliations

  1. College of Electronic Engineering, Nanjing XiaoZhuang University, Nanjing, People’s Republic of China

    Changliang Xu

  2. School of Microelectronics and Data Science, Anhui University of Technology, Maanshan, People’s Republic of China

    Hao Xu

  3. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China

    Zhong Yang, Jiying Wu & Luwei Liao

  4. Electric Power Research Institute of Guizhou Power Grid Co., Ltd, Guiyang, People’s Republic of China

    Qiuyan Zhang

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  1. Changliang Xu
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  2. Hao Xu
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  3. Zhong Yang
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  4. Jiying Wu
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  5. Luwei Liao
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Correspondence to Hao Xu.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10878-024-01142-w

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Xu, C., Xu, H., Yang, Z. et al. RETRACTED ARTICLE: Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot. J Comb Optim 46, 6 (2023). https://doi.org/10.1007/s10878-023-01071-0

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