$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly

TL;DR

This paper introduces an $ abla_1$ adaptive control scheme with switched reference models for Learn-to-Fly UAVs, enabling safe real-time learning and adaptation of vehicle dynamics through theoretical analysis and flight tests.

Contribution

It extends $ abla_1$ adaptive control to handle switched reference models with unknown parameters, integrating it into the L2F framework for UAVs with real-time learning.

Findings

Successful real-time UAV flight tests demonstrating adaptive control.

Theoretical guarantees for transient and steady-state performance.

Effective handling of model switching and parameter uncertainties.

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.