AERO-LQG: Aerial-Enabled Robust Optimization for LQG-Based Quadrotor Flight Controller
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916923688615936 |
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| author | Engelsman, Daniel Klein, Itzik |
| author_facet | Engelsman, Daniel Klein, Itzik |
| contents | Quadrotors are indispensable in civilian, industrial, and military domains, undertaking complex, high-precision tasks once reserved for specialized systems. Across all contexts, energy efficiency remains a critical constraint: quadrotors must reconcile the high power demands of agility with the minimal consumption required for extended endurance. Meeting this trade-off calls for mode-specific optimization frameworks that adapt to diverse mission profiles. At their core lie optimal control policies defining error functions whose minimization yields robust, mission-tailored performance. While solutions are straightforward for fixed weight matrices, selecting those weights is a far greater challenge-lacking analytical guidance and thus relying on exhaustive or stochastic search. This interdependence can be framed as a bi-level optimization problem, with the outer loop determining weights a priori. This work introduces an aerial-enabled robust optimization for LQG tuning (AERO-LQG), a framework employing evolutionary strategy to fine-tune LQG weighting parameters. Applied to the linearized hovering mode of quadrotor flight, AERO-LQG achieves performance gains of several tens of percent, underscoring its potential for enabling high-performance, energy-efficient quadrotor control. The project is available at GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AERO-LQG: Aerial-Enabled Robust Optimization for LQG-Based Quadrotor Flight Controller Engelsman, Daniel Klein, Itzik Systems and Control Quadrotors are indispensable in civilian, industrial, and military domains, undertaking complex, high-precision tasks once reserved for specialized systems. Across all contexts, energy efficiency remains a critical constraint: quadrotors must reconcile the high power demands of agility with the minimal consumption required for extended endurance. Meeting this trade-off calls for mode-specific optimization frameworks that adapt to diverse mission profiles. At their core lie optimal control policies defining error functions whose minimization yields robust, mission-tailored performance. While solutions are straightforward for fixed weight matrices, selecting those weights is a far greater challenge-lacking analytical guidance and thus relying on exhaustive or stochastic search. This interdependence can be framed as a bi-level optimization problem, with the outer loop determining weights a priori. This work introduces an aerial-enabled robust optimization for LQG tuning (AERO-LQG), a framework employing evolutionary strategy to fine-tune LQG weighting parameters. Applied to the linearized hovering mode of quadrotor flight, AERO-LQG achieves performance gains of several tens of percent, underscoring its potential for enabling high-performance, energy-efficient quadrotor control. The project is available at GitHub. |
| title | AERO-LQG: Aerial-Enabled Robust Optimization for LQG-Based Quadrotor Flight Controller |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.20888 |