cc-DRL: a Convex Combined Deep Reinforcement Learning Flight Control Design for a Morphing Quadrotor

TL;DR

This paper introduces cc-DRL, a novel control algorithm combining model-free deep reinforcement learning and convex combination techniques to manage the complex flight dynamics of morphing quadrotors with shape-changing capabilities.

Contribution

It proposes a new convex combined DRL control scheme for morphing quadrotors, addressing the challenge of their complex, hard-to-model flight dynamics.

Findings

Simulation results demonstrate the effectiveness of cc-DRL.

The proposed method improves flight control accuracy.

The approach is adaptable to different arm length modes.

Abstract

In comparison to common quadrotors, the shape change of morphing quadrotors endows it with a more better flight performance but also results in more complex flight dynamics. Generally, it is extremely difficult or even impossible for morphing quadrotors to establish an accurate mathematical model describing their complex flight dynamics. To figure out the issue of flight control design for morphing quadrotors, this paper resorts to a combination of model-free control techniques (e.g., deep reinforcement learning, DRL) and convex combination (CC) technique, and proposes a convex-combined-DRL (cc-DRL) flight control algorithm for position and attitude of a class of morphing quadrotors, where the shape change is realized by the length variation of four arm rods. In the proposed cc-DRL flight control algorithm, proximal policy optimization algorithm that is a model-free DRL algorithm is utilized to off-line train the corresponding optimal flight control laws for some selected representative arm length modes and hereby a cc-DRL flight control scheme is constructed by the convex combination technique. Finally, simulation results are presented to show the effectiveness and merit of the proposed flight control algorithm.