Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization

محورهای موضوعی : artificial intelligence

Reza Tarighi ¹ (Department of Electrical Engineering, University of Applied Science and Technology Tehran, Centre of Poolad Peech Kar (P.P.K), Tehran, Iran)
M.H. Kazemi ² (Department of Electrical Engineering, Shahed University, Tehran, Iran)
mohammad hosein khalesi ³ (Department of Mechanical Engineering, Semnan University, Semnan, Iran)

تاریخ دریافت : 1400/09/23 تاریخ پذیرش : 1401/02/03 تاریخ انتشار : 1401/06/10

کلید واژه: optimal path design, Particle swarm Algorithm, Optimization Algorithms, Predictive control, Genetic Algorithm,

چکیده مقاله :

In this paper, a new approach to the use of genetic algorithms and the predictive control method, for goal tracking is presented. A hypothetical rocket is modelled for the analyses. Rocket guidance algorithm is developed to achieve a desired mission goal according to some performance criteria and the imposed constraints. Given that goals can be fixed or moving, we have focused and expanded on this issue in this study and also the dynamic modelling of flying objects with six-degrees-of-freedom (DOF) is used to make the design more similar to the actual model. The predictive control method is used to predict the next step of rocket and aim movement. At each step of the problem, the rocket distance to the aim is obtained, and a trajectory is predicted to move the rocket towards the purpose. The objective function of this problem, in addition to the distance from the rocket position to the target, are also parameters of the dynamic model of the rocket. Therefore, these parameters are optimized at each step of the problem solving. Ultimately, the rocket strikes the intended aim by following this optimal path. Finally, for the validation of the model, numerical results are obtained for both Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Simulation results demonstrate the effectiveness and feasibility of the proposed optimization technique.

منابع و مأخذ:

• Shi, H., Chen, Z., Zhu, J., and Kuang, M., Model Predictive Guidance for Active Aircraft Protection from A Homing Missile, IET Control Theory Appl., Vol. 16, No. 2, 2022, pp. 208–218, doi: 10.1049/cth2.12218.


• Becan, M. R., Kuzucu, A., Fuzzy Predictive Pursuit Guidance in the Homing Missiles, Int. J. Inf. Technol., Vol. 1, No. 1, 2005, pp. 385–389.


• Bhattacharjee, D., Chakravarthy, A., and Subbarao, K., Nonlinear Model Predictive Control-Based Missile Guidance for Target Interception, in AIAA Scitech 2020 Forum, Vol. 1, Part. F, 2020, doi: 10.2514/6.2020-0865.


• Singh, L., Autonomous Missile Avoidance Using Nonlinear Model Predictive Control, In Collection of Technical Papers - AIAA Guidance, Navigation, and Control Conference, Vol. 2, 2004, pp. 1031–1045, doi: 10.2514/6.2004-4910.


• Optimization, U. N., Brief Papers Missile Guidance Law Based on Robust Model Predictive Control, Vol. 26, No. 8, 2015, pp. 1803–1809.


• Zhang, B., Zhou, D., Optimal Predictive Sliding-Mode Guidance Law for Intercepting Near-Space Hypersonic Maneuvering Target, Chinese J. Aeronaut., Vol. 35, No. 4, 2022, pp. 320–331, doi: 10.1016/j.cja.2021.05.021.


• Şumnu, A., Güzelbey, İ. H., and Öǧücü, O., Aerodynamic Shape Optimization of a Missile Using a Multiobjective Genetic Algorithm, Int. J. Aerosp. Eng., Vol. 2020, 2020, doi: 10.1155/2020/1528435.


• Alqudsi, Y. S., El-Bayoumi, G. M., Guidance Optimization for Tactical Homing Missiles and Air Defense Systems, in INCAS Bulletin, Vol. 10, No. 1, 2018, pp. 193–205, doi: 10.13111/2066-8201.2018.10.1.17.


• Klempay, O. T., Flight Test Point Optimization Program for a Self-Protection Application, 2022.


• Nocoń, Ł., Grzyb, M., Szmidt, P., Koruba, Z., and Nowakowski, Ł., Control Analysis with Modified LQR Method of Anti‐Tank Missile with Vectorization of the Rocket Engine Thrust, Energies, Vol. 15, No. 1, 2022, doi: 10.3390/en15010356.


• Zheng, X., Yang, S. C., and Zhang, K. Q., Design and Simulation of Trajectory Tracking Guidance Law Based on LQR for Target Missile, in IOP Conference Series: Materials Science and Engineering, Vol. 435, No. 1, 2018, pp. 0–8, doi: 10.1088/1757-899X/435/1/012021.


• Horla, D., Variational Calculus Approach to Optimal Interception Task of a Ballistic Missile in 1D and 2D Cases, Algorithms, Vol. 12, No. 8, 2019, pp. 1–15, doi: 10.3390/a12080148.


• Liu, X., Gu, Q., and Yang, C., Path Planning of Multi-Cruise Missile Based on Particle Swarm Optimization, In Proceedings-International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC, 2019, pp. 910–912, doi: 10.1109/SDPC.2019.00173.


• Ilan, B. D., Özd˙I. L., Trajectory Optimization of a Tactical Missile by Using, Middle East Technical University, The Degree of Master of Science Inaerospace Engineering, 2018.


• Anderson, M. B., Technology, S., Air, E., Base, F., Burkhalter, J. E., and Jenkins, R. M., Missile Aerodynamic Shape Optimization Using Genetic Algorithms, J. Spacecr. Rockets, Vol. 37, No. 5, 2000.


• Liu, H., Yu, L., Ruan, C., and Zhou, Z.,Tracking Air-to-Air Missile Using Proportional Navigation Model with Genetic Algorithm Particle Filter, Math. Probl, Eng., 2016, doi: 10.1155/2016/3921608.


• Huang, H., Zhao, X., and Zhang, X., Intelligent Guidance and Control Methods for Missile Swarm, Comput. Intell. Neurosci., 2022, doi: 10.1155/2022/8235148.


• Yu, J. F., Yang, S. C., and Zhang, Y. W., Flight Trajectory Correction of Target Missile and Design of Tracking Guidance, in Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE, No. Qr2mse, 2019, pp. 195–202, doi: 10.1109/QR2MSE46217.2019.9021272.


• Hartfield, R. J., Jenkins, R. M., and Burkhalter, J. E., Optimizing a Solid Rocket Motor Boosted Ramjet Powered Missile Using a Genetic Algorithm, Appl. Math. Comput., Vol. 181, No. 2, 2006, pp. 1720–1736, doi: 10.1016/j.amc.2006.03.028.


• Zhao, X., Fan, X., A Method Based on Genetic Algorithm for Anti-Ship Missile Path Planning, In Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO, Vol. 2, No. 3, 2009, pp. 156–159, doi: 10.1109/CSO.2009.370.


• Cribbs, H. B., Genetics-Based Trajectory Discovery for Tactical Missiles, In Collection of Technical Papers - AIAA 1st Intelligent Systems Technical Conference, Vol. 2, 2004, pp. 1052–1057, doi: 10.2514/6.2004-6550.


• Zheng, X., Gao, Y., Jing, W., and Wang, Y., Multidisciplinary Integrated Design of Long-Range Ballistic Missile Using PSO Algorithm, J. Syst. Eng. Electron., Vol. 31, No. 2, 2020, pp. 335–349, doi: 10.


• Arun, S. R., Sudharshan, P. P., Durai, A. R., Vertiselvan, S., and Yaswanth, K., Simulation of Non-Linear Missile Guidance System, Vol. 8, No. 7, 2020, pp. 6–9.


• Park, S., Deyst, J., and How, J. P., A New Nonlinear Guidance Logic for Trajectory Tracking, In Collection of Technical Papers - AIAA Guidance, Navigation, and Control Conference, Vol. 2, 2004, pp. 941–956, doi: 10.2514/6.2004-4900.


• Anderson, M. B., Burkhalter, J. E., and Jenkins, R. M., Design of a Guided Missile Interceptor Using a Genetic Algorithm, J. Spacecr. Rockets, Vol. 38, No. 1, 2001, pp. 28–35, doi: 10.2514/2.3668.


• Kassem, A. H., Trajectory Optimization via Simulation Using Incremented Genetic Algorithms, Can. Aeronaut, Space J., Vol. 52, 2006, pp. 15–20, doi: 10.5589/q06-005.


• Chartres, J. T. A., Trajectory Design, Optimization and Guidance for Reusable Launch Vehicles during the Terminal Area Flight Phase, 2007.


• Tekinalp, O., Bingol, M., Simulated Annealing for Missile Optimization: Developing Method and Formulation Techniques, J. Guid. Control. Dyn., Vol. 27, No. 4, 2004, pp. 616–626, doi: 10.2514/1.2103.


• Yang, Y. R., Jung, S. K., Cho, T. H., and Myong, R. S., An Aerodynamic Shape Optimization Study to Maximize the Range of a Guided Missile, 28th AIAA Appl. Aerodyn. Conf., Vol. 1, 2010, pp. 1–10, doi: 10.2514/6.2010-4240.


• Lisk, D., Robinson, T., and Robinson, D., Multi-Objective Optimization of Supersonic Projectiles Using Evolutionary Algorithms, 48th AIAA Aerosp. Sci. Meet. Incl. New Horizons Forum Aerosp. Expo., 2010, pp. 1–10, doi: 10.2514/6.2010-1500.


• Box, S., Bishop, C., and Hunt, H., A Stochastic Six-Degree-of-Freedom Flight Simulator for Passively Controlled High Power Rockets, J. Aerosp. Eng. - J Aerosp Eng, Vol. 24, 2010, doi: 10.1061/(ASCE)AS.1943-5525.0000051.


• Bachtiar, V., Mühlpfordt, T., Moase, W. H., Faulwasser, T., Findeisen, R., and Manzie, C., Nonlinear Model Predictive Missile Control with A Stabilising Terminal Constraint, In IFAC Proceedings Volumes (IFAC-PapersOnline), Vol. 19, No. Ldi, 2014, pp. 457–462, doi: 10.3182/20140824-6-za-1003.02122.


• Khamis, Y. Elhalwagy, F. A.E, Development of Fuzzy Logic Controller to Enhance the Performance of a HILS for a Homing Guided Missile, 2006.


• Parwana, H.,Varma, S. A., and Kothari, M., An SDRE Based Impact and Body Angle Constrained Guidance Against a Stationary Surface Target, IFAC-Papers Online, Vol. 49, No. 1, 2016, pp. 1–6, doi: 10.1016/j.ifacol.2016.03.019.


• Zipfel, P. H., Modeling and Simulation of Aerospace Vehicle Dynamics, Second Edition. American Institute of Aeronautics and Astronautics, 2007.


• Camacho E. F., and Bordons, C., Model Predictive Control - Second edition, 2007.


• Yang, G., A Modified Particle Swarm Optimizer Algorithm, IEEE, pp. 2675–2679, 1998.


• Hartfield, R. J., Jenkins, R. M., and Burkhalter, J. E., Ramjet Powered Missile Design Using a Genetic Algorithm, In AIAA Paper, 2004, pp. 814–824, doi: 10.2514/6.2004-451.


• Tewari, Atmospheric and Space Flight Dynamics: Modeling and Simulation with MATLAB and Simulink, 2007.