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520 Motor📖
1.About motor
  • About motor
2.ROS robot control board drive
  • 2.1 Control motor
  • 2.2 Timer captures encoder data
  • 2.3 PID control robot car
  • 2.4 Kinematic Analysis-Mecanum wheel structure
  • 2.5 Kinematic Analysis-Ackerman structure
  • 2.6 Kinematic Analysis-4WD structure
3.STM32 Self-balancing car tutorials
Get started quickly
  • 03.P, PI, PD controller theory
  • 01.Balancing car assembly
  • 02.Get started quicklyy
  • 03.Balance car interface description
  • 04.Common Problem Analysis
  • 05.Balance Car V2 Library Function Development Manual
Environment construction and development
  • 01.MDK-ARM installation
  • 02.STM32CubeIDE installation
  • 03.Common driver installation
  • 04.Download the program
  • 05.MDK-ARM project usage
  • 06.Program simulation
Robot car PID control course
  • 01.PID basic concept
  • 02.PID example analysis
  • 04.Position PID
  • 05.Incremental PID
  • 06.Cascade PID
  • 07.Balance principle of car
  • 08.Car upright control (PD)
  • 09.Car speed ​​control (PI)
  • 10.Car steering control (PD)
  • 11.Got angle and angular velocity (DMP algorithm)
  • 12.Got angle and angular velocity (Kalman filter algorithm)
  • 13.Got angle and angular velocity(Complementary filter algorithm)
Robot car basic course
  • 01.Car parameter adjustment
  • 02.Ultrasonic obstacle avoidance
  • 03.Ultrasonic follow
  • 04.Bluetooth remote control
  • 05.Load balance
Robot car LQR control
  • Robot car LQR control
Circuit design
  • 01.Overall circuit design
  • 02.STM32 minimum system
  • 03.Serial port circuit design
  • 04.Power circuit design
  • 05.MPU6050 Circuit Design
  • 06.Motor drive circuit design
Mathematical model
  • Balance car mathematical model
4.4-channel motor drive module
Introduction and Debug
  • 1.Introduction to 4-channel motor drive board
  • 2.Control command
  • 3.Motor introduction and usage
  • 4.PC host
IIC communication
  • For Arduino
  • For ESP32
  • For Jetson NANO
  • For MSPM0G3507
  • For MSPM0G3519new
  • For PICO2
  • For RDK X5
  • For RPi5
  • For STM32
USART communication
  • For Arduino
  • For ESP32
  • For Jetson NANO
  • For MSPM0G3507
  • For MSPM0G3519new
  • For PICO2
  • For RDK X5
  • For RPi5
  • For STM32
5.2-channel motor drive module
For MSPM0G3507
  • 1.AT8236 drive tutorials
  • 2.TB6612 drive tutorials
For MSPM0G3519
  • 1.AT8236 drive tutorialsnew
  • 2.TB6612 drive tutorialsnew
Download
  • Model File
  • Annex_ROS_robot_board
  • Annex_STM32_balancing_car
  • Annex_4channel_motor_drive
  • Annex_2channel_motor_drive
Download ZIP

Welcome to 520 Motor repository

02.PID example analysis

PID example analysis

PID example analysis Example Analysis 1Proportional (P) controllerIntegral (I) controllerDerivative (D) controllerSummary

The tutorial takes the electric heater control system as an example: its goal is to keep the temperature of a thermostat (such as a smart water dispenser) constant at 50°C.

PID formula

u(t)=Kpe(t)+Ki∫t0e(t)dt+Kdde(t)2

Example Analysis 1

Assuming that the current system temperature is 20°C, we need to use the PID controller to adjust the power of the heater so that the temperature reaches and maintains 50°C.

Proportional (P) controller

The main function is to quickly reduce the error.

Adjusting the proportional coefficient 𝐾𝑝 can make the heater power respond to the error quickly, thereby quickly raising the temperature to close to 50°C.

If 𝐾𝑝 is set larger, the temperature will rise quickly, but it will fluctuate greatly around 50°C.

If 𝐾𝑝 is set small, the temperature will rise slowly, but will fluctuate less around 50°C.

If only 𝐾𝑝 is adjusted, the system may oscillate around the set point

Integral (I) controller

The main function is to eliminate steady-state error.

Adjusting the integral coefficient 𝐾𝑖 can make the system gradually accumulate errors over a long period of time, eventually eliminating steady-state errors and stabilizing the temperature around 50°C.

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If only 𝐾𝑖 is adjusted, there may be no vibration, but the set point may not be reached quickly due to the excessive integral effect, resulting in a certain deviation between the temperature and the set point

Derivative (D) controller

The main function is to predict the trend of error changes.

Adjusting the differential coefficient 𝐾𝑑 can make the system predict the trend of error changes, thereby adjusting the heater power in advance, reducing the accumulation of errors, making the temperature approach 50°C faster and reducing overshoot.

Summary

In general, the reasonable selection of the values of the three parameters Kp, Ki, and Kd can make the system strike a balance between fast response, elimination of steady-state errors, and suppression of oscillations, thus achieving system stability and performance optimization.

PID basic concept
Position PID

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