mlsanityData to 7 Oct 2026

Real-time optimal control via Deep Neural Networks: study on landing problems

arXiv 1610.08668 ↗First posted 27 Oct 2016eess.SY cs.SYcited by 27

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Abstract (arXiv)

Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the on-board decision making system. In this paper this claim is investigated in more detail training deep artificial neural networks to represent the optimal control action during a pinpoint landing, assuming perfect state information. It is found to be possible to train deep networks for this purpose and that the resulting landings, driven by the trained networks, are close to simulated optimal ones. These results allow for the design of an on-board real time optimal control system able to cope with large sets of possible initial states while still producing an optimal response.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 5; 2022: 7; 2023: 1; 2024: 7; 2025: 2; 2026: 4.

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  1. 2025Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraintscited by 14
  2. 2022Neural representation of a time optimal, constant acceleration rendezvouscited by 10
  3. 2019Learning the optimal state-feedback via supervised imitation learningcited by 7
  4. 2021On the Universal Transformation of Data-Driven Models to Control Systemscited by 5
  5. 2021Advances in Trajectory Optimization for Space Vehicle Controlcited by 5
  6. 2022Selected Trends in Artificial Intelligence for Space Applicationscited by 5
  7. 2023Optimality Principles in Spacecraft Neural Guidance and Controlcited by 4
  8. 2024Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigationcited by 4
  9. 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order modelscited by 4
  10. 2024PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problemscited by 3
  11. 2024KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problemscited by 3
  12. 2022Neural Network Optimal Feedback Control with Guaranteed Local Stabilitycited by 2
15 more
  1. 2021Neural network optimal feedback control with enhanced closed loop stabilitycited by 1
  2. 2022Fast solver for J2-perturbed Lambert problem using deep neural networkcited by 1
  3. 2022Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradientcited by 1
  4. 2022Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifiercited by 1
  5. 2024Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifoldcited by 1
  6. 2021Deep L1 Stochastic Optimal Control Policies for Planetary Soft-landingcited by 0
  7. 2021Space Non-cooperative Object Active Tracking with Deep Reinforcement Learningcited by 0
  8. 2022Efficient low-thrust trajectory data generation based on generative adversarial networkcited by 0
  9. 2024Jacobian-Enhanced Neural Networkscited by 0
  10. 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order modelscited by 0
  11. 2025Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networkscited by 0
  12. 2026Pretrained Approximators for Low-Thrust Trajectory Cost and Reachabilitycited by 0
  13. 2026Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problemscited by 0
  14. 2026Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimizationcited by 0
  15. 2026Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamicscited by 0