Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations

TL;DR

This paper introduces a novel coordinated control approach for morphing aircraft using meta-learning to handle varying morphing conditions and coupled Riccati equations for stable flight and deformation control.

Contribution

It combines domain adversarial meta-learning with coupled state-dependent Riccati equations to achieve adaptive and stable control of morphing aircraft under different conditions.

Findings

Meta-learning effectively captures shared representations across morphing conditions.

The coupled Riccati equations provide stable feedback control solutions.

Simulation results validate the control strategy's efficacy.

Abstract

In this paper, the coordinated control problem of deformation and flight for morphing aircraft (MA) is studied by using meta-learning (ML) and coupled state-dependent Riccati equations (CSDREs). Our method is built on two principal observations that dynamic models of MA under varying morphing conditions share a morphing condition independent representation function and that the specific morphing condition part lies in a set of linear coefficients. To that end, the domain adversarially invariant meta-learning (DAIML) is employed to learn the shared representation with offline flight data. Based on the learned representation function, the coordinated control of the deformation and flight for MA is formulated as a non-cooperative differential game. The state-dependent feedback control solutions can be derived by addressing a pair of CSDREs. For this purpose, Lyapunov iterations are extended to obtain the positive semidefinite (definite) stabilizing solutions of the CSDREs, and the convergence proof of the proposed algorithm is provided. Finally, a simulation study is carried out to validate the efficacy of the developed coordinated game control strategies.