Co-Design of Aeroelastic Systems with Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → RL with diffusion and flow models
Abstract (arXiv)
Control co-design considers the physical system and its controller together, enabling the strong coupling between system design and control to be uncovered and exploited. This is especially relevant in aeroelastic flight systems, where structural, aerodynamic, and control design choices jointly determine manoeuvrability and efficiency. This paper presents a model-free nested co-design framework for aeroelastic systems using deep reinforcement learning, in which a design-conditioned control policy is trained with proximal policy optimisation while an outer loop updates a distribution over candidate design parameters. The approach is evaluated on three case studies of increasing complexity: a spring-mass-damper system, a pitch-plunge-flap aerofoil, and a highly flexible high-aspect-ratio glider performing a thermal-soaring mission in a stochastic environment. Across these case studies, the framework is shown to progressively concentrate the design search towards high-performing regions and to outperform policies trained on randomly sampled designs. The results also show that reward shaping plays an important role in enabling stable learning in partially observed and stochastic environments. In the final glider case, the method jointly addresses wing design, flight control, and mission-level behaviour in the presence of aeroelastic coupling and atmospheric uncertainty. These results highlight the potential of model-free co-design for complex aeroelastic systems in which design, control, and mission objectives are tightly coupled.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 60 references that are in the map. First posted on arXiv 16 Jul 2026.
Builds on
13 of 60 references.
References · 13
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2017Deep Reinforcement Learning: An Overviewcited by 333
- 2021Embodied Intelligence via Learning and Evolutioncited by 163
- 2020Flightmare: A Flexible Quadrotor Simulatorcited by 84
- 2018Benchmarking Reinforcement Learning Algorithms on Real-World Robotscited by 65
- 2018Jointly Learning to Construct and Control Agents using Deep Reinforcement Learningcited by 55
- 2020Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learningcited by 32
- 2024A Review of Nine Physics Engines for Reinforcement Learning Researchcited by 12
- 2023PyFlyt -- UAV Simulation Environments for Reinforcement Learning Researchcited by 9
- 2022N-LIMB: Neural Limb Optimization for Efficient Morphological Designcited by 8
- 2020Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesiscited by 7
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- 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
- 2024Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
- 2024A mechanism-driven reinforcement learning framework for shape optimization of airfoils
- 2025Digital Twin-enabled Multi-generation Control Co-Design with Deep Reinforcement Learning
- 2026Learning step-level dynamic soaring in shear flow ↗
- 2025Reinforcement Learning for Gliding Projectile Guidance and Control
- 2026High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
- 2026Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists ↗