Optimization imposition upon drone gimbal control electronics

  • Erhe Zheng Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
  • Timothy Sands Department of Mechanical Engineering (SCPD), Stanford University, Stanford, CA 94305, USA
Ariticle ID: 69
226 Views, 18 PDF Downloads
Keywords: proportional plus velocity controller, double integrator patching filter, control law inversion patching filter, real-time optimal control, open loop control, feedback control, Monte Carlo model

Abstract

The goal of the manuscript is to design a relatively good control structure for the noise suppression of a drone’s camera gimbal action. The gimbal’s movement can be simplified as a rest-to-rest reorientation system that can achieve the boundary result of a dynamic system. Six different control architectures are proposed and evaluated based on their ability to control the trajectory of the dynamic-system position and speed, their running time, and their quadratic cost. The robustness of the control architecture to uncertainties in inertia and sensor noise is also analyzed. Monte Carlo figures are used to assess the performance of the six control systems. The conditions for applying different architectures are identified through this analysis. The analysis and experimental tests reveal the most suitable control of the drone’s camera gimbal rotation.

References

[1] Shokirov R, Abdujabarov N, Jonibek T, et al. Prospects of the development of unmanned aerial vehicles (UAVs). Technical Science and Innovation 2020; 2020(3): 4–8. doi: 10.51346/tstu-01.20.3-77-0069

[2] Saha A, Kumar A, Sahu AK. FPV drone with GPS used for surveillance in remote areas. In: Proceedings of the 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN); 3–5 November 2017; Kolkata, India. pp. 62–67.

[3] Jain U, Rogers M, Matson ET. Drone forensic framework: Sensor and data identification and verification. In: Proceedings of the 2017 IEEE Sensors Applications Symposium (SAS); 13–15 March 2017; Glassboro, NJ, USA. pp. 1–6.

[4] Mogili UR, Deepak BBVL. Review on application of drone systems in precision agriculture. Procedia Computer Science 2018; 133: 502–509. doi: 10.1016/j.procs.2018.07.063

[5] Reinecke M, Prinsloo T. The influence of drone monitoring on crop health and harvest size. In: Proceedings of the 2017 1st International Conference on Next Generation Computing Applications (NextComp); 19–21 July 2017; Mauritius. pp. 5–10.

[6] Almalki FA, Aljohani M, Algethami M, Soufiene BO. Incorporating drone and AI to empower smart journalism via optimizing a propagation model. Sustainability 2022; 14(7): 3758. doi: 10.3390/su14073758

[7] Quiroz G, Kim SJ. A confetti drone: Exploring drone entertainment. In: Proceedings of the 2017 IEEE International Conference on Consumer Electronics (ICCE); 8–10 January 2017; Las Vegas, NV, USA. pp. 378–381.

[8] Koehle K. NASA offers guidance for drone use viewing Antares launch. Available online: https://www.nasa.gov/wallops/2021/press-release/nasa-offers-guidance-for-drone-use-viewing-antares-launch (accessed on 11 February 2021).

[9] Huang C, Yang Z, Kong Y, et al. Learning to capture a film-look video with a camera drone. In: Proceedings of the 2019 international conference on robotics and automation (ICRA); 20–24 May 2019; Montreal, QC, Canada. pp. 1871–1877.

[10] Ansias Carmona E. Development of Active Camera Stabilization System for Implementation on UAV’s [Bachelor’s thesis]. Universitat Politècnica de Catalunya; 2012.

[11] Cong Danh N. The stability of a two-axis gimbal system for the camera. The Scientific World Journal 2021; 2021: 9958848. doi: 10.1155/2021/9958848

[12] Gašparović M, Jurjević L. Gimbal influence on the stability of exterior orientation parameters of UAV acquired image. Sensor 2017; 17(2): 401. doi: 10.3390/s17020401

[13] Kim M, Byun GS, Kim GH, Choi MH. The stabilizer design for a drone-mounted camera gimbal system using intelligent-PID controller and tuned mass damper. International Journal of Control and Automation 2016; 9(5): 387–394. doi: 10.14257/ijca.2016.9.5.37

[14] Rajesh RJ, Ananda CM. PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal. In: Proceedings of the 2015 International Conference on Power and Advanced Control Engineering (ICPACE); 12–14 August 2015; Bengaluru, India. pp. 128–133.

[15] Duan Q, Zhou X, He Q, et al. Pointing control design based on the PID type-III control loop for two-axis gimbal systems. Sensors and Actuators A: Physical 2021; 331: 112923. doi: 10.1016/j.sna.2021.112923

[16] Laššák M, Draganova K, Blišťanová M, et al. Small UAV camera gimbal stabilization using digital filters and enhanced control algorithms for aerial survey and monitoring. Acta Montanistica Slovaca 2020; 25(1): 127–137. doi: 10.46544/AMS.v25i1.12

[17] Altan A, 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 2020; 138: 106548. doi: 10.1016/j.ymssp.2019.106548

[18] Aguilar WG, Angulo C. Real-time model-based video stabilization for microaerial vehicles. Neural Processing Letters 2016; 43(2): 459–477. doi: 10.1007/s11063-015-9439-0

[19] Kangunde V, Jamisola RS, Theophilus EK. A review on drones controlled in real-time. International Journal of Dynamics and Control 2021; 9: 1832–1846. doi: 10.1007/s40435-020-00737-5

[20] Altan A, Aslan Ö, Hacıoğlu R. Real-time control based on NARX neural network of hexarotor UAV with load transporting system for path tracking. In: Proceedings of the 2018 6th International Conference on Control Engineering & Information Technology (CEIT); 25–27 October 2018; Istanbul, Turkey. pp. 1–6.

[21] Yoo H, Kim B, Kim JW, Lee JH. Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation. Computers & Chemical Engineering 2021; 144: 107133. doi: 10.1016/j.compchemeng.2020.107133

[22] Binder K. Applications of Monte Carlo methods to statistical physics. Reports on Progress in Physics 1997; 60(5): 487. doi: 10.1088/0034-4885/60/5/001

[23] Sands T. Inducing performance of commercial surgical robots in space. Sensors 2023; 23(3): 1510. doi: 10.3390/s23031510

[24] Mingkhwan E, Khawsuk W. Digital image stabilization technique for fixed camera on small size drone. In: Proceedings of the 2017 Third Asian Conference on Defence Technology (ACDT); 18–20 January 2017; Phuket, Thailand. pp. 12–19.

[25] Guermond JL, Nazarov M, Popov B, Tomas I. Second-order invariant domain preserving approximation of the Euler equations using convex limiting. SIAM Journal on Scientific Computing 2018; 40(5): A3211–A3239. doi: 10.1137/17M1149961

[26] Qin S, Cramer M, Koch C, Serafini A. Optimal control for Hamiltonian parameter estimation in non-commuting and bipartite quantum dynamics. SciPost Physics 2022; 13(6): 121. doi: 10.21468/SciPostPhys.13.6.121

[27] Breitenbach T, Borzì A. A sequential quadratic Hamiltonian method for solving parabolic optimal control problems with discontinuous cost functionals. Journal of Dynamical and Control Systems 2019; 25(3): 403–435. doi: 10.1007/s10883-018-9419-6

[28] Jafarizadeh MA, Naghdi F, Bazrafkan MR. Time optimal control of two-level quantum systems. Physics Letters A 2020; 384(29): 126743. doi: 10.1016/j.physleta.2020.126743

[29] Alpago D, Dörfler F, Lygeros J. An extended Kalman filter for data-enabled predictive control. IEEE Control Systems Letters 2020; 4(4): 994–999. doi: 10.1109/LCSYS.2020.2998296

[30] Ross IM. A Primer on Pontryagin’s Principle in Optimal Control. Collegiate Publishers; 2015.

[31] Yang ZC, Rahmani A, Shabani A, et al. Optimizing variational quantum algorithms using Pontryagin’s minimum principle. Physical Review X 2017; 7(2): 021027. doi: 10.1103/PhysRevX.7.021027

[32] Wang Y, Wu Z, Chen Y, et al. Research on energy optimization control strategy of the hybrid electric vehicle based on Pontryagin’s minimum principle. Computers & Electrical Engineering 2018; 72: 203–213. doi: 10.1016/j.compeleceng.2018.09.018

[33] Dagdougui H, Ouammi A, Sacile R. Optimal control of a network of power microgrids using the Pontryagin’s minimum principle. IEEE Transactions on Control Systems Technology 2014; 22(5): 1942–1948. doi: 10.1109/TCST.2013.2293954

Published
2023-07-25
How to Cite
Zheng, E., & Sands, T. (2023). Optimization imposition upon drone gimbal control electronics. Journal of AppliedMath, 1(2), 69. https://doi.org/10.59400/jam.v1i2.69
Section
Article