python實現PID
- 2020 年 1 月 10 日
- 筆記
最近搗鼓ROS的時候，發現github上有人用python實現了PID，雖然可能執行效率不高，但是用python寫工具的時候還是很方便的。從github上把程式碼搬下來，簡單分析一下
給程式碼: 在截個都看煩了的公式意思一下吧
#!/usr/bin/python # # This file is part of IvPID. # Copyright (C) 2015 Ivmech Mechatronics Ltd. <[email protected]> # # IvPID is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # IvPID is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # title :PID.py # description :python pid controller # author :Caner Durmusoglu # date :20151218 # version :0.1 # notes : # python_version :2.7 # ============================================================================== """Ivmech PID Controller is simple implementation of a Proportional-Integral-Derivative (PID) Controller in the Python Programming Language. More information about PID Controller: http://en.wikipedia.org/wiki/PID_controller """ import time class PID: """PID Controller """ def __init__(self, P=0.2, I=0.0, D=0.0): self.Kp = P self.Ki = I self.Kd = D self.sample_time = 0.00 self.current_time = time.time() self.last_time = self.current_time self.clear() def clear(self): """Clears PID computations and coefficients""" self.SetPoint = 0.0 self.PTerm = 0.0 self.ITerm = 0.0 self.DTerm = 0.0 self.last_error = 0.0 # Windup Guard self.int_error = 0.0 self.windup_guard = 20.0 self.output = 0.0 def update(self, feedback_value): """Calculates PID value for given reference feedback .. math:: u(t) = K_p e(t) + K_i int_{0}^{t} e(t)dt + K_d {de}/{dt} .. figure:: images/pid_1.png :align: center Test PID with Kp=1.2, Ki=1, Kd=0.001 (test_pid.py) """ error = self.SetPoint - feedback_value self.current_time = time.time() delta_time = self.current_time - self.last_time delta_error = error - self.last_error if (delta_time >= self.sample_time): self.PTerm = self.Kp * error self.ITerm += error * delta_time if (self.ITerm < -self.windup_guard): self.ITerm = -self.windup_guard elif (self.ITerm > self.windup_guard): self.ITerm = self.windup_guard self.DTerm = 0.0 if delta_time > 0: self.DTerm = delta_error / delta_time # Remember last time and last error for next calculation self.last_time = self.current_time self.last_error = error self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm) def setKp(self, proportional_gain): """Determines how aggressively the PID reacts to the current error with setting Proportional Gain""" self.Kp = proportional_gain def setKi(self, integral_gain): """Determines how aggressively the PID reacts to the current error with setting Integral Gain""" self.Ki = integral_gain def setKd(self, derivative_gain): """Determines how aggressively the PID reacts to the current error with setting Derivative Gain""" self.Kd = derivative_gain def setWindup(self, windup): """Integral windup, also known as integrator windup or reset windup, refers to the situation in a PID feedback controller where a large change in setpoint occurs (say a positive change) and the integral terms accumulates a significant error during the rise (windup), thus overshooting and continuing to increase as this accumulated error is unwound (offset by errors in the other direction). The specific problem is the excess overshooting. """ self.windup_guard = windup def setSampleTime(self, sample_time): """PID that should be updated at a regular interval. Based on a pre-determined sampe time, the PID decides if it should compute or return immediately. """ self.sample_time = sample_time
注釋很完美，沒有能解釋的啊
待會寫個程式，用plot畫個圖線，驗證一下
