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The “Fast Adaptation and Learning for Control Online” (FALCON) control architecture from Aurora Flight Sciences, a Boeing company, has undergone testing on the waters of the Charles River in Boston, Massachusetts.
Designed for a variety of complex platforms, including air and land vehicles, the adaptive control architecture can operate both as an advisor and as an aid to current platforms.
The architecture can also perform when fully integrated, as demonstrated in the Defense Advanced Research Projects Agency’s (DARPA) Learning Introspective Control (LINC) program, where 2 autonomous vehicles operate collaboratively.
Aurora’s latest tests recreated the conditions that lead to a 2021 incident which blocked the Suez Canal for six days, involving a vessel named Ever Given.
Aurora constructed a virtual canal scaled to a small unmanned surface vehicle (USV). A simulated Venturi effect, alike the one induced in the 2021 scenario, pushed the USV toward the banks of the virtual canal. The Aurora team simultaneously triggered a thruster failure and remotely deployed a sail to increase wind loading.
The tests demonstrated relative vessel-to-vessel station keeping for an underway replenishment (UNREP) scenario and automated passage through the Suez Canal. In each scenario an autonomously controlled USV employed adaptive control to overcome a variety of disturbances while completing the mission.
The autonomous system was challenged with disturbances including thruster failure and Venturi effects. With these disturbances, the conventional controller failed, while Aurora’s controller quickly regained vehicle control and provided safe vehicle operation through the virtual canal.
Aurora state that the Venturi effect is difficult to model, and conventional controllers failed to compensate for it. Aurora’s control architecture, however, successfully compensated for the effect to complete the mission successfully.
Underway replenishment, or UNREP, enables resupply of US Navy ships at sea, allowing those ships to always remain mission-ready, wherever they may be. Typically, this task requires helmsmen on both the receiving vessel and the delivery vessel to perform the mission manually. Using the small USV, Aurora state that they have modeled how this task could be accomplished autonomously.
Aurora, teamed with the Massachusetts Institute of Technology (MIT) Aerospace Controls Laboratory and the MIT Marine Autonomy Laboratory (PavLab), is developing and testing machine learning-based introspection technologies that enable uncrewed vehicles and surface vessels to adapt their control laws as they encounter conditions not predicted at the time of vehicle design. The vehicles autonomously reconstitute control for safe, continued operation and successful completion of tasks.
Work on the FALCON program is ongoing, and Aurora say that it is looking forward to deploying the architecture on larger vessels this spring.
