UNDER CONSTRUCTION!
This project aims to build a drone entirely from scratch, with a primary focus on developing custom software. The aim is to better understand different control architectures for drones as well as possible state estimation techniques that are not commonly used in commercial drones.
Simulation
As a first step for this project, a simulation in Matlab Simulink was developed. The simulation is based on Mathematical Modelling of Unmanned Aerial Vehicles with Four Rotors.
The simulation was developed in order to test different control techniques as well as different state estimators for pose estimation. The video and images below illustrate the outcome of the simulation. The state estimators as well as the controllers are compared in the section “Experiments”.
The simulation contains various physical influences, going from drag on different parts of the mechanics all the way to wind gusts and battery voltage drop. For further details on the simulation, feel free to get in touch!
A visualization was created using Simscape Multibody in order to get a better feeling for the drones behaviour.
The video above shows the drone going into a hover position at 1 meter height with random wind gusts from all directions of 3 m/s.
The video below shows the drone flying a circle, while maintaining the correct yaw angle again with wind gusts of 3 m/s. The results were achieved using a linear Kalman Filter for the IMU and a PD controller for each DOF. The vectors displayed are a visualization of the wind force acting on the drone.
The graph above display the result when tracking a circular trajectory on the XY-plane. The results show a substantial steady state tracking error. This could either be reduced by adding an integral term to the PD controller, or by using another control architecture. For this project, the goal is not to get the most lightweight and easy to implement controller, but rather to learn about new controller architectures, that can match or even beat the performance of a PID. Therefore, I also implemented an LQR controller as well as a non-linear-MPC in simulation. The non-linear-MPC is expected to required very high computational power on the hardware, and is therefore only implemented in simulation. The MPC was designed with 12 states, 12 inputs and 4 outputs.
Hardware
Beyond the software, the drone’s frame was designed and manufactured using 3D printing techniques. The electronic hardware is currently in its prototype phase and has not been optimized specifically for drone applications. Below are the components selected for the project:
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Drone Controller (Brain): STM32-G431KB
Selected for its affordability, relatively high computational power (170 MHz), versatility, compact size, low power consumption, and multiple connectivity options including 4x PWM pins, SPI, and I2C interfaces. -
Motors: EMAX ECO II 1700 KV
Chosen based on compatibility with the propeller and battery size. -
Propellers: 7×3.5×3 (7-inch, 3-blade)
Designed to deliver higher thrust, as power consumption optimization was not a priority in this project. -
ESC (Electronic Speed Controllers): 40A PWM-compatible
Capable of handling the required current for motor control. -
Battery: 4S 2200 mAh
Provides sufficient power to meet the system’s energy demands. -
IMU (Inertial Measurement Unit): Invensense GY-87
A low-cost, low-power sensor used for motion tracking and stabilization. -
Barometer: Bosch BMP380
Selected for its high accuracy in pressure sensing. -
Transmitter: Turnigy TGY-9X
Used for remote control. -
Receiver: Turnigy IA6C Mini
A compact and reliable receiver compatible with the transmitter. -
ESP32 Mini
Used for wifi and bluetooth connectivity to send an receive commands -
HC-SR04
Ultrasonic sensor mounted beneath the drone in order to measure the height with higher precision -> Fused with a Kalman Filter with the barometer of the IMU
This integrated approach ensures a blend of custom design, prototyping, and practical component selection to achieve the project’s goals.
The low voltage electronics are connected as follows:
