Swarm-optimized Adaptive Augmentation of Missile Autopilot
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911144910782464 |
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| author | Dorsey, Alexander Oveissi, Parham Barton, Jeffrey D. Goel, Ankit |
| author_facet | Dorsey, Alexander Oveissi, Parham Barton, Jeffrey D. Goel, Ankit |
| contents | This paper considers the problem of optimizing a missile autopilot. In particular, the paper investigates the application of an online learning technique to learn and optimize the gains of a three-loop topology autopilot for a planar missile modeled with nonlinear dynamics and nonlinear aerodynamics forces and moments. The classical autopilot for a missile is based on a three-loop topology, where each loop consists of tunable proportional gains. An adaptive three-loop autopilot is constructed by augmenting the classical autopilot's fixed-gain controllers with a learning-based controller, which is recursively optimized using retrospective cost optimization. Numerical simulations show that online learning improves the tracking performance of the classical autopilot in both nominal and off-nominal interception scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Swarm-optimized Adaptive Augmentation of Missile Autopilot Dorsey, Alexander Oveissi, Parham Barton, Jeffrey D. Goel, Ankit Systems and Control This paper considers the problem of optimizing a missile autopilot. In particular, the paper investigates the application of an online learning technique to learn and optimize the gains of a three-loop topology autopilot for a planar missile modeled with nonlinear dynamics and nonlinear aerodynamics forces and moments. The classical autopilot for a missile is based on a three-loop topology, where each loop consists of tunable proportional gains. An adaptive three-loop autopilot is constructed by augmenting the classical autopilot's fixed-gain controllers with a learning-based controller, which is recursively optimized using retrospective cost optimization. Numerical simulations show that online learning improves the tracking performance of the classical autopilot in both nominal and off-nominal interception scenarios. |
| title | Swarm-optimized Adaptive Augmentation of Missile Autopilot |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.07748 |