$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly
Year: 2021
Type: preprint
Abstract: Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control. To ensure safe learning of the vehicle dynamics on the fly, this paper presents an $\mathcal{L}_1$ adaptive control ($\mathcal{L}_1$AC) based scheme, which actively estimates and compensates for the discrepancy between the intermediately learned dynamics and the actual dynamics. First, to incorporate the periodic update of the learned model within the L2F framework, this paper extends the $\mathcal{L}_1$AC architecture to handle a switched reference system subject to unknown time-varying parameters and disturbances. The paper also includes an analysis of both transient and steady-state performance of the $\mathcal{L}_1$AC architecture in the presence of non-zero initialization error for the state predictor. Second, the paper presents how the proposed $\mathcal{L}_1$AC scheme is integrated into the L2F framework, including its interaction with the baseline controller and the real-time modeling module. Finally, flight tests on an unmanned aerial vehicle (UAV) validate the efficacy of the proposed control and learning scheme.
Source: arXiv (Cornell University)
Authors: Snyder, Steven, Zhao, Pan, Hovakimyan, Naira
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1523888516, G2808327302, G5611334394, WE WILL DEVELOP AN INITIAL CAPABILITY THAT WILL ALLOW DYNAMIC OPTIMIZATION BETWEEN MISSION PERFORMANCE AND ACOUSTIC SIGNATURE FOR A REPRESENTATIVE TILT-WING VTOL VEHICLE WITH DISTRIBUTED ELECTRIC PROPULSION WHILE INCORPORATING COLLISION AVOIDANCE ALGORITHMS THAT RETAIN ORIGINAL MISSION OBJECTIVES. WE WILL CONSIDER OFF-NOMINAL CONDITIONS THAT INCLUDE PROPULSION FAILURES MOVING OBSTACLES AND SEVERE GUSTS. WE WILL ALSO INCORPORATE UNCERTAINTIES ASSOCIATED WITH DETECTION OF OBSTACLES ACOUSTIC SIGNATURE AND PROPULSIVE EFFICIENCY. TWO TYPES OF OPTIMIZATION FRAMEWORKS WILL BE EXPLORED. THE FIRST ONE WILL BE FORMULATED IN THE SPIRIT OF NON-ZERO SUM GAME WHERE THE INTEREST IN THE TWO OBJECTIVES WILL NOT BE IN CONFLICT E.G. MAINTAIN CERTAIN ACOUSTIC SIGNATURE AND TO GET TO A DESTINATION WITHIN A LARGE TIME WINDOW. THE SECOND FORMULATION WILL BE FORMULATED WITHIN THE SPIRIT OF ZERO-SUM GAME E.G. MINIMIZE ACOUSTIC SIGNATURE AND MINIMIZE TIME TO DESTINATION. IN CASE OF PROPULSIVE FAILURE BECAUSE THERE IS REDUNDANCY OF PROPULSION DEVICES IT MIGHT STILL BE POSSIBLE TO OPTIMIZE BETWEEN MINIMUM ACOUSTIC SIGNATURE AND SAFE AIRCRAFT FLIGHT WITH SLIGHTLY DEGRADED PERFORMANCE.
OpenAlex ID: W4301250214