pid.py >>https://github.com/wagnerc4/flight_controller/blob/master/pid.py

openmv 官网：http://book.openmv.cc/project/zhui-xiao-qiu-de-xiao-8f665d28-project-pan-tilt-md.html

缘由：官网上只调PID中p参数，让当时小白的我有点误解，学过PID，但不熟悉应用人就会一头雾水，所以就有本文咯，实际上我们可以充分利用强大的PID。 

本文正确标题：如何用PID去控制追球小车的方向和速度控制。

硬件：两轮小车+OpenMV主控

目标：摄像头找红色小球色块，如果没有，小车绕圆运动寻找，发现小球后小车直线运动到距离球20cm。

内容：本文主要讲述如何用PID算法去实现对电机的控制。

注意：博主电机并无编码盘，输入误差是摄像头判断的数据。假设大家已经用官网的car.py作过了对电机的处理。

代码解释：

整定，就是修改PID，两个，一个方向，一个速度，因为旋转找小球

x_pid = PID(p=0.8, i=0.1, d=0.1,imax=10)#2018年10月26日更新
h_pid = PID(p=0.03, i=0.005,d=0.1, imax=40)

 

    while(True):
        pass
        clock.tick() # Track elapsed milliseconds between snapshots().
        img = sensor.snapshot() # Take a picture and return the image.

        blobs = img.find_blobs([green_threshold])
        if blobs:
            max_blob = find_max(blobs)
            x_error = max_blob[5]-img.width()/2
            h_error = max_blob[2]*max_blob[3]-size_threshold
            if -20<h_error<20 and -10<x_error<10:
                break
            print("x error: ", x_error) 
            img.draw_rectangle(max_blob[0:4]) # rect
            img.draw_cross(max_blob[5], max_blob[6]) # cx, cy
            x_output=x_pid.get_pid(x_error,1)
            h_output=h_pid.get_pid(h_error,1)
            print("h_output",h_output)
            car.run(-h_output-x_output,-h_output+x_output)
        else:
            car.run(40,-40)#绕圆旋转

 

附官网PID.py文件：

from pyb import millis
from math import pi, isnan

class PID:
  _kp = _ki = _kd = _integrator = _imax = 0 
  _last_error = _last_derivative = _last_t = 0
  _RC = 1/(2 * pi * 20)
  def __init__(self, p=0, i=0, d=0, imax=0):
    self._kp = float(p)  #比例
    self._ki = float(i)   #积分
    self._kd = float(d)   #微分
    self._imax = abs(imax)  
    self._last_derivative = float('nan')
  def get_pid(self, error, scaler):
    tnow = millis()
    dt = tnow - self._last_t
    output = 0
    if self._last_t == 0 or dt > 1000:
      dt = 0
      self.reset_I()
    self._last_t = tnow
    delta_time = float(dt) / float(1000)
    output += error * self._kp
    if abs(self._kd) > 0 and dt > 0:
      if isnan(self._last_derivative):
        derivative = 0
        self._last_derivative = 0
      else:
        derivative = (error - self._last_error) / delta_time
      derivative = self._last_derivative + \
                   ((delta_time / (self._RC + delta_time)) * \
                    (derivative - self._last_derivative))
      self._last_error = error
      self._last_derivative = derivative
      output += self._kd * derivative
    output *= scaler
    if abs(self._ki) > 0 and dt > 0:
      self._integrator += (error * self._ki) * scaler * delta_time
      if self._integrator < -self._imax: self._integrator = -self._imax
      elif self._integrator > self._imax: self._integrator = self._imax
      output += self._integrator
    return output
  def reset_I(self):
    self._integrator = 0
    self._last_derivative = float('nan')

附博主main.py代码

# Blob Detection Example
#
# This example shows off how to use the find_blobs function to find color
# blobs in the image. This example in particular looks for dark green objects.

import sensor, image, time
import car
from pid import PID

# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
green_threshold   = (18, 69, 40, 86, 0, 72)
size_threshold = 2300
x_pid = PID(p=0.8, i=0.1, d=0.1,imax=10)#2018年10月26日更新
h_pid = PID(p=0.03, i=0.005,d=0.1, imax=40)


#x_pid = PID(p=0.8, i=0.1, imax=10)
#h_pid = PID(p=0.03, i=0.005, imax=40)

def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

while(True):

    while(True):
        pass
        clock.tick() # Track elapsed milliseconds between snapshots().
        img = sensor.snapshot() # Take a picture and return the image.

        blobs = img.find_blobs([green_threshold])
        if blobs:
            max_blob = find_max(blobs)
            x_error = max_blob[5]-img.width()/2
            h_error = max_blob[2]*max_blob[3]-size_threshold
            if -20<h_error<20 and -10<x_error<10:
                break
            print("x error: ", x_error) 
            img.draw_rectangle(max_blob[0:4]) # rect
            img.draw_cross(max_blob[5], max_blob[6]) # cx, cy
            x_output=x_pid.get_pid(x_error,1)
            h_output=h_pid.get_pid(h_error,1)
            print("h_output",h_output)
            car.run(-h_output-x_output,-h_output+x_output)
        else:
            car.run(40,-40)#绕圆旋转

        

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