Intelligent control of quad-rotor aircrafts with a STM32 microcontroller using deep neural networks

Abstract

Purpose Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope. Design/methodology/approach In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system. Findings This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft. Originality/value This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

Application of Microcontroller-Based Multipath Servos in Industrial Robot Control Systems

“…It is very important to control the action period of the servo; too fast or too slow will lead to diferent degrees of consequences. After the study, it was decided that the PWM period required for the control of the servo is set to 20 ms [20]. At the same time, the frequency of the PWM wave is set to 50 Hz, and the value of the frequency setting is calculated by the following formula:…”

Guest editorial

“…Although deep neural networks have been successfully implemented in computers with powerful computation, it is rarely deployed in STM32 microcontrollers. Guan et al make a good attempt in Guan et al (2021) to implement intelligent control for quad-rotor aircrafts with a STM32 microcontroller using deep neural networks. A 32-bit micro-controller STM32F103C8T6 is adopted as the main control chip for the control system of the quad-rotor aircraft, and a deep neural network is deployed.…”

Position and Attitude Control Based on Single Neuron PID With Gravity Compensation for Quad Rotor UAV

“…In recent years, the quad rotor UAV has been widely used in military, remote sensing, surveying, mapping, and many other fields (Guan et al 2021;Gu et al 2021). Because the quad rotor UAV has the characteristics of strong coupling, nonlinearity and under actuation, the control method has been one of main research directions.…”

“…Because the quad rotor UAV has the characteristics of strong coupling, nonlinearity and under actuation, the control method has been one of main research directions. At present, many control methods, such as PID control (Miranda-Colorado and Aguilar 2020; Rosales et al 2018), sliding mode control (Mofid and Mobayen 2018;Oba et al 2018;Zhang et al 2021), intelligent control (Guan et al 2021), backstepping control (Zhou et al 2018), L1 Adaptive control (Souanef 2023), fault tolerant control (Di et al 2023), and robust control (Weng et al 2022;Yang 2021) have been applied to the control of quad rotor UAV. Though the traditional PID controller is easily used to implement the control of the quad rotor UAV, the fixed PID parameters cannot adapt to the changes of external conditions and control object parameters, which makes it difficult to achieve the desired control effect.…”