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

Miguel F. Arevalo-Castiblanco, Yejin Wi, Marzia Cescon and, Cesar A. Uribe

TL;DR

Problem addressed: robust multi-agent synchronization in drone networks with parameter variations (the reality gap). Approach: implement a distributed model reference adaptive control (DMRAC) where each follower uses an adaptive MRAC law with gains $k_m$ and $k_r$ guided by a reference model with dynamics $\dot{x}_m = A_m x_m + B_m r$. Key contributions: an experimental framework including PRBS-based system identification yielding $G_{vz}(s)$, a baseline PID for the leader, and five experiments assessing tracking, communication, energy, and scalability; results show MRAC provides robust synchronization and reduced control energy. Significance: demonstrates practical viability of DM-RAC for heterogeneous UAV networks, enabling longer mission durations and robust operation under payload changes and network delays.

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.

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

TL;DR

Problem addressed: robust multi-agent synchronization in drone networks with parameter variations (the reality gap). Approach: implement a distributed model reference adaptive control (DMRAC) where each follower uses an adaptive MRAC law with gains and guided by a reference model with dynamics . Key contributions: an experimental framework including PRBS-based system identification yielding , a baseline PID for the leader, and five experiments assessing tracking, communication, energy, and scalability; results show MRAC provides robust synchronization and reduced control energy. Significance: demonstrates practical viability of DM-RAC for heterogeneous UAV networks, enabling longer mission durations and robust operation under payload changes and network delays.

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.
Paper Structure (10 sections, 2 theorems, 22 equations, 14 figures, 1 table)

This paper contains 10 sections, 2 theorems, 22 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Quadrotor Dynamics and Kinematics
  3. System Architecture
  4. Dynamic Model
  5. Leader drone parameterization
  6. System Identification
  7. Baseline PID Controller
  8. Followers Adaptive Control Design
  9. Comparative Performance of PID and MRAC Controllers
  10. Conclusions

Key Result

Proposition 1

Let Assumption assum:feedback-mc hold, and consider a drone $x_1$ with dynamics eq1 communicates with a leader drone $x_m$ with dynamics eq2. Then, the control law eq6a with the adaptive laws k1s guarantees that the synchronization error $e_1=x_1-x_m$ is bounded.

Figures (14)

  • Figure 1: (a) Quadrotor body frame; angles and angular velocities. (b) Block diagram DMRAC drone implementation.
  • Figure 2: Software architecture for implementation in ANT-X drones.
  • Figure 3: (a) Identification data. (b) Singular values.
  • Figure 4: (a) Cross-validation. (b) Verification of identified vertical dynamic.
  • Figure 5: Block diagram for altitude control.
  • ...and 9 more figures

Theorems & Definitions (2)

  • Proposition 1: Chapter 5 in Nguyen2018L
  • Proposition 2: Theorem 1 in Baldi2019