Design of Motor Speed Control System Based on STM32 Microcontroller

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chong Zhao, Zhaoxi Hua
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引用次数: 1

Abstract

With the application of embedded technology in servo motor control, the motor speed regulation method has gradually changed from analog to digital ones. Especially, DC motor control with ARM has become a new trend. We design a PID speed regulation system based on an STM32 microcontroller. The system includes an STM32F103C8T6 minimum system, power module, motor drive module, key module, MX1508 double H bridge, speed measurement module, TFT display module, and others. The design adopts the principle of pulse modulation for DC motor speed regulation based on STM32 internal PWM signal, output to MX1508 double H bridge motor drive module for driving amplification, and then drive signal output to DC motor. The duty ratio of the PWM signal is adjusted to achieve the motor steering and speed control. The system has a simple structure, strong anti-interference ability, and high cost-performance. MATLAB simulation results show that the speed regulation of PID DC motor based on the STM32 microcontroller is better than the traditional control strategy and has a good application prospect.
基于STM32单片机的电机调速系统设计
随着嵌入式技术在伺服电机控制中的应用,电机调速方式逐渐由模拟调速向数字调速转变。特别是基于ARM的直流电动机控制已成为一种新的发展趋势。本文设计了一种基于STM32单片机的PID调速系统。该系统包括STM32F103C8T6最小系统、电源模块、电机驱动模块、按键模块、MX1508双H桥、测速模块、TFT显示模块等。本设计采用脉冲调制原理对直流电动机进行调速,基于STM32内部PWM信号,输出到MX1508双H桥电机驱动模块进行驱动放大,再将驱动信号输出到直流电动机。通过调节PWM信号的占空比,实现电机的转向和转速控制。该系统结构简单,抗干扰能力强,性价比高。MATLAB仿真结果表明,基于STM32单片机的PID直流电动机调速策略优于传统控制策略,具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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