Development of an intelligent online closed-loop trajectory generation algorithm for a satellite attitude control system

Document Type : selected article

Authors
Milad Kamzan 1
Mana Ghanifar 2
AmirAli Nikkhah 3
Jafar Roshanian 4
Mohammad Teshnehlab 5
1 PhD.Student of K. N. Toosi University of Technology - Intelligent Control Systems Institute
2 PhD Student, Faculty of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Associate professor, Faculty of K. N. Toosi University of Technology, Tehran, Iran
4 Professor, Faculty of K. N. Toosi University of Technology, Tehran, Iran
5 Professor, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
10.22034/jssta.2023.405810.1131
Abstract
In this article, a new closed-loop algorithm is presented to generate an optimal angular trajectory for a given satellite to reach the desired final point. Using the capabilities of artificial neural networks, this algorithm can find the best trajectory to reach the final setpoints based on the dynamic behavior of the system and the preset controller capability by using the desired final values of the trajectory and the values of the system state variables at each simulation time. In the presence of external disturbances, this closed-loop intelligent trajectory generation algorithm shows advanced adaptive performance, which allows it to develop the best alternative trajectory to achieve the final setpoint and return the system to the main trajectory. Despite the fact that this algorithm is able to restore the main trajectory, it is also capable of preventing unreasonable control efforts by considering the control properties of the system. This intelligent algorithm of angular path generation shows high accuracy and effective performance after simulations are performed in the MATLAB software environment with predefined external disturbances
Keywords
Intelligent closed#loop angular trajectory generation algorithm#Satellite attitude maneuver#Intelligent trajectory generation# Autoencoders# Artificial neural networks
Subjects
[1]           V. Schneider and F. Holzapfel, “Modular Trajectory Generation Test Platform for Real Flight Systems,” in Advances in Aerospace Guidance, Navigation and Control, Cham: Springer International Publishing, 2018, pp. 185–202. doi: 10.1007/978-3-319-65283-2_10.
[2]           V. Y. Hernandez Marquez, R. S. Nunez Cruz, J. M. Ibarra Zannatha, and C. Enriquez Ramirez, “Optimal trajectories generation for autonomous navigation tasks in mobile robots,” in 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, Sep. 2018, pp. 1–6. doi: 10.1109/ICEEE.2018.8533936.
[3]           S. Tang and V. Kumar, “Safe and complete trajectory generation for robot teams with higher-order dynamics,” IEEE International Conference on Intelligent Robots and Systems, vol. 2016-November, pp. 1894–1901, Nov. 2016, doi: 10.1109/IROS.2016.7759300.
[4]           C. Richter, A. Bry, and N. Roy, “Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments,” 2016, pp. 649–666. doi: 10.1007/978-3-319-28872-7_37.
[5]           S. Tang and V. Kumar, “Mixed Integer Quadratic Program trajectory generation for a quadrotor with a cable-suspended payload,” Proc IEEE Int Conf Robot Autom, vol. 2015-June, no. June, pp. 2216–2222, Jun. 2015, doi: 10.1109/ICRA.2015.7139492.
[6]           F. Gao, W. Wu, Y. Lin, and S. Shen, “Online Safe Trajectory Generation for Quadrotors Using Fast Marching Method and Bernstein Basis Polynomial,” Proc IEEE Int Conf Robot Autom, pp. 344–351, Sep. 2018, doi: 10.1109/ICRA.2018.8462878.
[7]           D. Mellinger, N. Michael, and V. Kumar, “Trajectory generation and control for precise aggressive maneuvers with quadrotors,” Int J Rob Res, vol. 31, no. 5, pp. 664–674, Apr. 2012, doi: 10.1177/0278364911434236.
[8]           M. W. Mueller, M. Hehn, and R. Dandrea, “A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation,” IEEE Transactions on Robotics, vol. 31, no. 6, pp. 1294–1310, Dec. 2015, doi: 10.1109/TRO.2015.2479878.
[9]           S. Liu et al., “Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments,” IEEE Robot Autom Lett, vol. 2, no. 3, pp. 1688–1695, Jul. 2017, doi: 10.1109/LRA.2017.2663526.
[10]         D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in 2011 IEEE International Conference on Robotics and Automation, IEEE, May 2011, pp. 2520–2525. doi: 10.1109/ICRA.2011.5980409.
[11]         H. Hua and Y. Fang, “A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor,” IEEE Trans Neural Netw Learn Syst, pp. 1–12, 2022, doi: 10.1109/TNNLS.2022.3217814.
[12]         X. Zhang, X. Zhang, Z. Lu, and W. Liao, “Optimal Path Planning-Based Finite-Time Control for Agile CubeSat Attitude Maneuver,” IEEE Access, vol. 7, pp. 102186–102198, 2019, doi: 10.1109/ACCESS.2019.2927401.
[13]         M. Zarourati, M. Mirshams, and M. Tayefi, “Attitude path design and adaptive robust tracking control of a remote sensing satellite in various imaging modes,” Proc Inst Mech Eng G J Aerosp Eng, vol. 237, no. 9, pp. 2166–2184, Jul. 2023, doi: 10.1177/09544100221148887.
[14]         C. Guo, K. Kidono, and M. Ogawa, “Learning-based trajectory generation for intelligent vehicles in urban environment,” in 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE, Jun. 2016, pp. 1236–1241. doi: 10.1109/IVS.2016.7535548.
[15]         R. Ahmad, S. Tichadou, and J.-Y. Hascoet, “3D safe and intelligent trajectory generation for multi-axis machine tools using machine vision,” Int J Comput Integr Manuf, vol. 26, no. 4, pp. 365–385, Apr. 2013, doi: 10.1080/0951192X.2012.717720.
[16]         M. J. Sidi, Spacecraft dynamics and control: a practical engineering approach. Cambridge university press, 1997.
 

  • Receive Date 06 July 2023
  • Revise Date 19 November 2023
  • Accept Date 16 January 2024