A Data-Driven Approach to Integrated Adaptive Morphing and Guidance for Cruise Missiles

Abstract

To address the complex coupling between aerodynamic characteristics and guidance control for morphing flight missiles, this study proposes a data-driven approach to integrated adaptive morphing and guidance. Firstly, an aerodynamic surrogate model is constructed using a fully connected neural network (FCNN), mapping the configuration parameters to aerodynamic parameters. Secondly, an adaptive physical parameters optimization network (PPON) is developed to optimize aerodynamic characteristics based on predictions from the aerodynamic surrogate model. Thirdly, an integrated morphing and guidance model is derived by applying the proximal policy optimization (PPO) algorithm from deep reinforcement learning (DRL), embedded with the adaptive aerodynamic optimization model. Eventually, the proposed integrated approach is applied to the guidance task of a morphing cruise missile with variable camber wings. Simulation results demonstrate that the integrated guidance model significantly enhances aerodynamic performance and generates more continuous guidance commands within approximately 4.3 s, outperforming the deep Q-Network (DQN) algorithm under morphing flight conditions. Moreover, compared to the PPO and DQN-based guidance laws without morphing flight conditions, the integrated model improves both the guidance accuracy and terminal kinetic energy. Furthermore, the integrated guidance model, trained on stationary targets, remains effective for engaging moving and maneuvering targets, showcasing its robust generalization capability.

Keywords

adaptive morphing / cruise missile / data-driven / deep reinforcement learning / integrated guidance

References

[1] S. He, D. Lin, and J. Wang, “Continuous Second-Order Sliding Mode Based Impact Angle Guidance Law,” Aerospace Science and Technology41 (2015): 199-208.

[2] S. He, T. Song, and D. Lin, “Impact Angle Constrained Integrated Guidance and Control for Maneuvering Target Interception,” Journal of Guidance, Control, and Dynamics40 (2017): 2653-2661.

[3] K. Hovell and S. Ulrich, “On Deep Reinforcement Learning for Spacecraft Guidance,” in AIAA Scitech 2020 Forum (2020), 1600, https://doi.org/10.2514/6.2020-1600.

[4] S. He, H.-S. Shin, and A. Tsourdos, “Computational Missile Guidance: A Deep Reinforcement Learning Approach,” Journal of Aerospace Information Systems18 (2021): 571-582.

[5] R. Furfaro, A. Scorsoglio, R. Linares, and M. Massari, “Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach,” Acta Astronautica171 (2020): 156-171.

[6] V. Shalumov, “Cooperative Online Guide-Launch-Guide Policy in a Target-Missile-Defender Engagement Using Deep Reinforcement Learning,” Aerospace Science and Technology104 (2020): 105996.

[7] B. Gaudet, R. Linares, and R. Furfaro, “Adaptive Guidance and Integrated Navigation With Reinforcement Meta-Learning,” Acta Astronautica169 (2020): 180-190.

[8] B. Gaudet, R. Furfaro, and R. Linares, “Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets,” Aerospace Science and Technology99 (2020): 105746.

[9] N. Wang, X. Wang, N. Cui, Y. Li, and B. Liu, “Deep Reinforcement Learning-Based Impact Time Control Guidance Law With Constraints on the Field-of-View,” Aerospace Science and Technology128 (2022): 107765.

[10] X. Gong, W. Chen, and Z. Chen, “All-Aspect Attack Guidance Law for Agile Missiles Based on Deep Reinforcement Learning,” Aerospace Science and Technology127 (2022): 107677.

[11] M. Wu, X. He, Z. Qiu, and Z. Chen, “Guidance Law of Interceptors Against a High-Speed Maneuvering Target Based on Deep Q-Network,” Transactions of the Institute of Measurement and Control44 (2022): 1373-1387.

[12] L. Chu, Q. Li, F. Gu, X. Du, Y. He, and Y. Deng, “Design, Modeling, and Control of Morphing Aircraft: A Review,” Chinese Journal of Aeronautics35 (2022): 220-246.

[13] M. S. Parancheerivilakkathil, J. S. Pilakkadan, M. Amoozgar, D. Asadi, Y. Zweiri, and M. I. Friswell, “A Review of Control Strategies Used for Morphing Aircraft Applications,” Chinese Journal of Aeronautics37 (2024): 436-463, https://doi.org/10.1016/j.cja.2023.12.035.

[14] X. Li, X. Wang, H. Zhou, and Y. Li, “A Novel Evasion Guidance for Hypersonic Morphing Vehicle via Intelligent Maneuver Strategy,” Chinese Journal of Aeronautics37 (2024): 441-461.

[15] C. Bao, P. Wang, and G. Tang, “Integrated Method of Guidance, Control and Morphing for Hypersonic Morphing Vehicle in Glide Phase,” Chinese Journal of Aeronautics34 (2021): 535-553.

[16] K. Shukla, V. Oommen, A. Peyvan, et al., “Deep Neural Operators as Accurate Surrogates for Shape Optimization,” Engineering Applications of Artificial Intelligence129 (2024): 107615, https://doi.org/10.1016/j.engappai.2023.107615.

[17] V. Raul and L. Leifsson, “Surrogate-Based Aerodynamic Shape Optimization for Delaying Airfoil Dynamic Stall Using Kriging Regression and Infill Criteria,” Aerospace Science and Technology111 (2021): 106555.

[18] S. H. Berguin, D. Rancourt, and D. N. Mavris, “Method to Facilitate High-Dimensional Design Space Exploration Using Computationally Expensive Analyses,” AIAA Journal53 (2015): 3752-3765, https://doi.org/10.2514/1.J054035.

[19] Z. J. Grey and P. G. Constantine, “Active Subspaces of Airfoil Shape Parameterizations,” AIAA Journal56 (2018): 2003-2017, https://doi.org/10.2514/1.J056054.

[20] Y. Qiu, J. Bai, N. Liu, and C. Wang, “Global Aerodynamic Design Optimization Based on Data Dimensionality Reduction,” Chinese Journal of Aeronautics31 (2018): 643-659.

[21] H. Tang, Y. Lei, X. Li, K. Gao, and Y. Li, “Aerodynamic Shape Optimization of a Wavy Airfoil for Ultra-Low Reynolds Number Regime In Gliding Flight,” Energies13 (2020): 467.

[22] R.-L. Liu, Y. Hua, Z.-F. Zhou, Y. Li, W.-T. Wu, and N. Aubry, “Prediction and Optimization of Airfoil Aerodynamic Performance Using Deep Neural Network Coupled Bayesian Method,” Physics of Fluids34 (2022): 117116.

[23] M.-Y. Wu, X.-Y. Yuan, Z.-H. Chen, W.-T. Wu, Y. Hua, and N. Aubry, “Airfoil Shape Optimization Using Genetic Algorithm Coupled Deep Neural Networks,” Physics of Fluids35 (2023): 085140, https://doi.org/10.1063/5.0160954.

[24] X. Yan, J. Zhu, M. Kuang, and X. Wang, “Aerodynamic Shape Optimization Using a Novel Optimizer Based on Machine Learning Techniques,” Aerospace Science and Technology86 (2019): 826-835, https://doi.org/10.1016/j.ast.2019.02.003.

[25] J. Li, M. Zhang, J. R. R. A. Martins, and C. Shu, “Efficient Aerodynamic Shape Optimization With Deep-Learning-Based Geometric Filtering,” AIAA Journal58 (2020): 4243-4259, https://doi.org/10.2514/1.J059254.

[26] G. Achour, W. J. Sung, O. J. Pinon, and D. N. Mavris, “Development of a Conditional Generative Adversarial Network for Airfoil Shape Optimization,” AIAA Scitech 2020 Forum1, no. PartF (2020): 2261, https://doi.org/10.2514/6.2020-2261.

[27] J. Li, M. Zhang, C. M. J. Tay, et al., “Low-Reynolds-Number Airfoil Design Optimization Using Deep-Learning-Based Tailored Airfoil Modes,” Aerospace Science and Technology121 (2022): 107309.

[28] J. Li, X. Du, and J. R. R. A. Martins, “Machine Learning in Aerodynamic Shape Optimization,” Progress in Aerospace Sciences134 (2022): 100849, https://doi.org/10.1016/j.paerosci.2022.100849.

[29] A. Ahmad, M. R. Saad, A. C. Idris, M. R. A. Rahman, and S. Sujipto, “Aerodynamic Performances of Cruise Missile Flying Above Local Terrain,” in IOP Conference Series: Materials Science and Engineering, (IOP Publishing 2016), 12006.

[30] A. S. Utegen, K. Moldamurat, M. Ainur, A. Talgat, A. G. Amandykuly, and S. S. Brimzhanova, “Development and Modeling of Intelligent Control System of Cruise Missile Based on Fuzzy Logic,” in 2021 16th IEEE International Conference on Control, Electronics and Computer Technology, (IEEE, 2021), pp. 1-6.

[31] A. Jameson, W. Schmidt, and E. Turkel, “Numerical Solution of the Euler Equations by Finite Volume Methods Using Runge Kutta Time Stepping Schemes,” in 14th Fluid and Plasma Dynamics Conference (1981), 1259.

[32] H. Xie, J. Wang, and M. Zhang, “Parametric Generative Schemes With Geometric Constraints for Encoding and Synthesizing Airfoils,” Engineering Applications of Artificial Intelligence128 (2024): 107505, https://doi.org/10.1016/j.engappai.2023.107505.

[33] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ArXiv Prepr, ArXiv1412.6980, 2014.

[34] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” ArXiv Prepr, ArXiv1707.06347 2017.

[35] S. Kakade and J. Langford, “Approximately Optimal Approximate Reinforcement Learning,” in Proceedings of the 19th International Conference on Machine Learning (2002), 267-274.

RIGHTS & PERMISSIONS

2025 The Author(s). International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.