T. Zhang, Q. Li, C.-S. Zhang, H.-W. Liang, P. Li, T.-M. Wang, S. Li, Y.-L. Zhu, and C. Wu, ‘‘Current trends in the development of intelligent unmanned autonomous systems,’’ Frontiers Inf. Technol. Electron. Eng., vol. 18, no. 1, pp. 68–85, Jan. 2017.
L. Fridman, A. Poznyak, and F. J. Bejarano, Robust Output LQ Optimal Control via Integral Sliding Modes. New York, NY, USA: Springer, 2014.
A. I. Bhatti, ‘‘Advanced sliding mode controllers for industrial applications,’’ Dept. Eng., University of Leicester, Leicester, U.K., Tech. Rep. U531381, 1998.
A. Banerjee, M. Mukherjee, S. Satpute, and G. Nikolakopoulos, ‘‘Resiliency in space autonomy: A review,’’ Current Robot. Rep., vol. 4, no. 1, pp. 1–12, Mar. 2023.
C. Lewicki, P. Diamandis, E. Anderson, C. Voorhees, and F. Mycroft, ‘‘Planetary resources—The asteroid mining company,’’ New Space, vol. 1, no. 2, pp. 105–108, Jun. 2013.
D. Hestroffer et al., ‘‘Small solar system bodies as granular media,’’ Astron. Astrophys. Rev., vol. 27, no. 1, pp. 1–64, 2019.
E. Hand, ‘‘Philae probe makes bumpy touchdown on a comet,’’ Science, vol. 346, no. 6212, pp. 900–901, Nov. 2014.
S. J. N. Lexau, M. Breivik, and A. M. Lekkas, ‘‘Automated docking for marine surface vessels—A survey,’’ IEEE Access, vol. 11, pp. 132324–132367, 2023.
M. A. Alandihallaj, N. Assadian, and R. Varatharajoo, ‘‘Finite-time asteroid hovering via multiple-overlapping-horizon multiple-model predictive control,’’ Adv. Space Res., vol. 71, no. 1, pp. 645–653, Jan. 2023.
M. A. A. Hallaj and N. Assadian, ‘‘Sliding mode control of electromagnetic tethered satellite formation,’’ Adv. Space Res., vol. 58, no. 4, pp. 619–634, Aug. 2016.
O. Bertozzi, H. R. Chamorro, E. O. Gomez-Diaz, M. S. Chong, and S. Ahmed, ‘‘Application of data-driven methods in power systems analysis and control,’’ IET Energy Syst. Integr., vol. 6, no. 3, pp. 197–212, Sep. 2024.
T. T. Nguyen, N. D. Nguyen, and S. Nahavandi, ‘‘Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,’’ IEEE Trans. Cybern., vol. 50, no. 9, pp. 3826–3839, Sep. 2020.
X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, ‘‘Deep reinforcement learning: A survey,’’ IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 4, pp. 5064–5078, Apr. 2022.
M. Ramezani, M. A. Amiri Atashgah, and A. Rezaee, ‘‘A fault-tolerant multi-agent reinforcement learning framework for unmanned aerial vehicles–unmanned ground vehicle coverage path planning,’’ Drones, vol. 8, no. 10, p. 537, Sep. 2024.
M. Ramezani, H. Habibi, J. L. Sanchez-Lopez, and H. Voos, ‘‘UAV path planning employing MPC-reinforcement learning method considering collision avoidance,’’ in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), Jun. 2023, pp. 507–514.
M. Ramezani, M. A. Atashgah, J. L. Sanchez-Lopez, and H. Voos, ‘‘Human-centric aware UAV trajectory planning in search and rescue missions employing multi-objective reinforcement learning with AHP and similarity-based experience replay,’’ in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), Jun. 2024, pp. 177–184.
M. Ramezani and M. A. Amiri Atashgah, ‘‘Energy-aware hierarchical reinforcement learning based on the predictive energy consumption algorithm for search and rescue aerial robots in unknown environments,’’ Drones, vol. 8, no. 7, p. 283, Jun. 2024.
J. Elkins, R. Sood, and C. Rumpf, ‘‘Adaptive continuous control of spacecraft attitude using deep reinforcement learning,’’ in Proc. AAS/AIAA Astrodynamics Specialist Conf., Reston, VA, USA, 2020, pp. 420–475.
J. Wu, Q. M. J. Wu, S. Chen, F. Pourpanah, and D. Huang, ‘‘A-TD3: An adaptive asynchronous twin delayed deep deterministic for continuous action spaces,’’ IEEE Access, vol. 10, pp. 128077–128089, 2022.
P. Yu, Z. Wang, H. Zhang, and Y. Song, ‘‘Safe reinforcement learning for power system control: A review,’’ 2024, arXiv:2407.00681.
S. Khoroshylov and M. Redka, ‘‘Deep learning for space guidance, navigation, and control,’’ Space Sci. Technol., vol. 27, no. 6, pp. 38–52, 2021.
G. Shetty, M. Ramezani, H. Habibi, H. Voos, and J. L. Sanchez-Lopez, ‘‘Motion control in multi-rotor aerial robots using deep reinforcement learning,’’ in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), May 2025, pp. 29–36.
M. Ramezani, H. Habibi, and H. Voos, ‘‘UAV path planning employing MPC-reinforcement learning method considering collision avoidance,’’ 2023, arXiv:2302.10669.
C. Mu, S. Liu, M. Lu, Z. Liu, L. Cui, and K. Wang, ‘‘Autonomous spacecraft collision avoidance with a variable number of space debris based on safe reinforcement learning,’’ Aerosp. Sci. Technol., vol. 149, Jun. 2024, Art. no. 109131.
M. Ramezani, M. Alandihallaj, and A. M. Hein, ‘‘Fuel-efficient and fault-tolerant CubeSat orbit correction via machine learning-based adaptive control,’’ Aerospace, vol. 11, no. 10, p. 807, Sep. 2024.
M. Ramezani, M. A. Alandihallaj, B. C. Yaçın, M. A. O. Mendez, and H. Voos, ‘‘MPC-based deep reinforcement learning method for space robotic control with fuel sloshing mitigation,’’ 2025, arXiv:2509.21045.
M. Ramezani, M. Amin Alandihallaj, M. A. Olivares Mendez, and A. M. Hein, ‘‘Fuel-aware autonomous docking using RL-augmented MPC rewards for on-orbit refueling,’’ Acta Astronautica, vol. 238, pp. 690–705, Jan. 2026.
M. Ramezani, M. Atashgah, M. Alandihallaj, and A. Hein, ‘‘Reinforcement learning for planning and task coordination in a swarm of CubeSats: Overcoming processor limitation challenges,’’ in Proc. Int. Astron. Congr., 2023, pp. 1–12.
M. Ramezani, M. Amin Alandihallaj, J. Luis Sanchez-Lopez, and A. Hein, ‘‘Safe hierarchical reinforcement learning for CubeSat task scheduling based on energy consumption,’’ 2023, arXiv:2309.12004.
M. Alandihallaj, M. Ramezani, and A. Hein, ‘‘MBSE-enhanced LSTM framework for satellite system reliability and failure prediction,’’ in Proc. 12th Int. Conf. Model-Based Softw. Syst. Eng., Aland, 2024, pp. 349–356.
Y. Chow, O. Nachum, A. Faust, E. A. Duéñez-Guzmán, and M. Ghavamzadeh, ‘‘Safe policy learning for continuous control,’’ in Proc. Conf. Robot Learn., 2020, pp. 801–821.
M. Guerrier, K. Soma, H. Fouad, and G. Beltrame, ‘‘Guided by guardrails: Control barrier functions as safety instructors for robotic learning,’’ 2025, arXiv:2505.18858.
L. Zhao, K. Gatsis, and A. Papachristodoulou, ‘‘Stable and safe reinforcement learning via a barrier-Lyapunov actor-critic approach,’’ in Proc. 62nd IEEE Conf. Decis. Control (CDC), Dec. 2023, pp. 1320–1325.
S. Mosharafian, S. Afzali, Y. Bao, and J. M. Velni, ‘‘A deep reinforcement learning-based sliding mode control design for partially-known nonlinear systems,’’ in Proc. Eur. Control Conf. (ECC), Jul. 2022, pp. 2241–2246.
O. Youssef, M. Wafa, and R. Shalaby, ‘‘Reinforcement learning-enhanced adaptive sliding mode control for nonlinear systems,’’ Complex Intell. Syst., vol. 11, no. 8, pp. 1–18, Aug. 2025.
J. Yao, M. Han, and X. Yin, ‘‘Lyapunov-based distributed reinforcement learning control with stability guarantee,’’ Comput. Chem. Eng., vol. 195, Apr. 2025, Art. no. 108979.
J. V. Carneiro, C. Allard, and H. Schaub, ‘‘Rotating rigid body dynamics architecture for spacecraft simulation software implementation,’’ in Proc. AAS Guid. Control Conf., Breckenridge, CO, USA, 2023, pp. 1–12.
D. Byzov, P. Martyshko, and A. Chernoskutov, ‘‘Computationally effective modeling of self-demagnetization and magnetic field for bodies of arbitrary shape using polyhedron discretization,’’ Mathematics, vol. 10, no. 10, p. 1656, May 2022.
G. Shetty, M. Ramezani, H. Habibi, H. Voos, and J. L. Sanchez-Lopez, ‘‘Motion control in multi-rotor aerial robots using deep reinforcement learning,’’ 2025, arXiv:2502.05996.
S. Dankwa and W. Zheng, ‘‘Twin-delayed DDPG: A deep reinforcement learning technique to model a continuous movement of an intelligent robot agent,’’ in Proc. 3rd Int. Conf. Vis., Image Signal Process., Aug. 2019, pp. 1–5.
S. M. Amrr and A. Alturki, ‘‘Robust control design for an active magnetic bearing system using advanced adaptive SMC technique,’’ IEEE Access, vol. 9, pp. 155662–155672, 2021.
R. Wiśniewski, ‘‘Sliding mode attitude control for magnetic actuated satellite,’’ IFAC Proc. Volumes, vol. 31, no. 21, pp. 179–184, Aug. 1998.
P. Magnusson, M. A. Barucci, R. P. Binzel, C. Blanco, M. Di Martino, J. D. Goldader, M. Gonano-Beurer, A. W. Harris, T. Michałowski, S. Mottola, D. J. Tholen, and W. Z. Wisniewski, ‘‘Asteroid 951 gaspra: Pre-galileo physical model,’’ Icarus, vol. 97, no. 1, pp. 124–129, May 1992.
M. G. Kivelson, L. F. Bargatze, K. K. Khurana, D. J. Southwood, R. J. Walker, and P. J. Coleman, ‘‘Magnetic field signatures near Galileo’s closest approach to gaspra,’’ Science, vol. 261, no. 5119, pp. 331–334, Jul. 1993.
J. Ďurech, V. Sidorin, and M. Kaasalainen, ‘‘DAMIT: A database of asteroid models,’’ Astron. Astrophys., vol. 513, p. A46, Apr. 2010.
