An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks

TL;DR

This paper introduces a distributed model reference adaptive control strategy for drone networks, improving synchronization, energy efficiency, and robustness compared to traditional controllers, demonstrated through experimental vertical velocity control results.

Contribution

It develops a novel adaptive control method for multi-agent drone synchronization that adapts to model differences and enhances performance over classical PID controllers.

Findings

Adaptive control reduces energy consumption.

Improves synchronization accuracy.

Outperforms classical PID in experiments.

Abstract

This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior.