Ultra-Lightweight DEA Drone Flight Controller Development

Tue, 04 Mar 2025 00:00:00 +0000

Since March 2025, I have been collaborating with Professor Huichan Zhao to develop an innovative flight control system for an ultra-lightweight drone utilizing soft artificial muscles. The drone weighs only 23 grams and employs Dielectric Elastomer Actuators (DEA) as its primary propulsion mechanism.

Project Overview

This research focuses on overcoming the unique challenges of controlling drones powered by soft artificial muscles. Unlike traditional motor-based systems, DEA actuators offer silent operation, high energy efficiency, and biomimetic movement patterns, but require specialized control approaches due to their nonlinear electromechanical properties.

Flight Controller Development

I am designing a custom flight controller architecture specifically optimized for DEA-driven drones:

Implemented real-time control algorithms accounting for DEA hysteresis and viscoelastic behavior
Developed adaptive PID controllers with gain scheduling for voltage-to-strain conversion
Integrated sensor fusion for IMU data processing with Kalman filtering

Simulator Implementation

To enable safe testing and rapid prototyping, I built a physics-based simulator featuring:

High-fidelity DEA actuator models with electromechanical coupling dynamics
Aerodynamic modeling for ultra-lightweight frames
Real-time visualization of drone state and actuator deformation

Technical Challenges

The project addresses several key research challenges:

Nonlinear Control: Compensating for DEA’s voltage-dependent strain response and creep effects
Weight Constraints: Implementing full control stack within 23g total system mass

Current Progress

As of today, we have achieved:

Successful closed-loop attitude control in simulation with <5° steady-state error
Preliminary flight tests in mujoco simulation, demonstrating basic stabilization capabilities

[Project repository link to be added upon publication]

UTAT-ADR Autonomous Drone Racing Research

Fri, 20 Sep 2024 00:00:00 +0000

From September till December, 2024, I joint a team in UTIAS, University of Toronto, which mainly focus on Autonomous Drone Racing.

Under professor Hugh H. T. Liu’s guidance, I mainly focus on building a simulator for the drone, also planned the yaw angle.

Research Overview

Human pilots can use their own talent to controll drones to fly rapidly through gates, and the goal of the whole team is to use computer-based autonomous pilot to guide the drone.

My Contribution

Building a simulation system

The team previously used Betaflight to be its flight controller. However, to make full use of AI, we need to simulate how the drone behaves. So, developing a simulator according to Betaflight’s original code is very important.

I read all the lines from Betaflight, and extracted the controller code and test it. Also, I tested the input signal and output RPM curve.

This task is not a hard one, but it took me a lot of time to get to know how Betaflight work. As an open-source code, it’s super long and hard to read.

The following is the code structure of Betaflight that I organized：

This one is a demo of the part I contributed for the simulator:

Yaw planning

The racing drone we built is using computer vision to guide and locate itself. The drone need to look at the “gate” to get to know where it is.

My work is to help the drone get better vision to have a more accurate position.

The following is a demo of the yaw-planning. The red arrow is the yaw angle of a world-champion level human-pilot, while the blue arrow is the planned one. The black line is the gate.

Click here to see the orinigal code.

Drone | Zilin Chen's Homepage