Simple (but complete) PID controller in Python
Project description
PID_Py
PID_Py
provide a PID controller wrote in Python. This PID controller is simple to use, but it's complete.
:bangbang: Non-responsability :bangbang:
I am not responsible for any material or personal damages in case of failure. Use at your own risk.
Installation
python3 -m pip install PID_Py
Usage
Minimum usage
from PID_Py.PID import PID
# Initialization
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0)
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
In this usage the PID as no limitation, no history and the PID is in direct action (Error increasing -> Increase output).
Indirect action PID
If you have a system that required to decrease command to increase feedback, you can use indirectAction
parameters.
from PID_Py.PID import PID
# Initialization
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0, indirectAction = True)
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
Limiting output
If your command must be limit you can use outputLimits
parameters.
from PID_Py.PID import PID
# Initialization
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0, outputLimits = (0, 100))
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
By default the value is (None, None)
, wich implies that there is no limits. You can activate just the maximum limit with (None, 100)
. The same for the minimum limit (-100, None)
.
Historian
If you want to historize PID values, you can configure the historian to record values.
from PID_Py.PID import PID
from PID_Py.PID import HistorianParameters
# Initialization
historianParameters = HistorianParamters.SETPOINT | HistorianParameters.PROCESS_VALUE
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0, historianParameters = HistorianParameters)
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
...
# PID Historian
import matplotlib.pyplot as plt
plt.plot(pid.historian["TIME"], pid.historian["SETPOINT"], label="Setpoint")
plt.plot(pid.historian["TIME"], pid.historian["PROCESS_VALUE"], label="Process value")
plt.legend()
plt.show()
In the example above, the PID historian records setpoint
, processValue
and time
. Time is the elapsed time from the start. After that a graphic is draw with matplotlib
.
Historian parameters list
P
: proportionnal partI
: integral partD
: derivative partERROR
: PID errorSETPOINT
: PID setpointPROCESS_VALUE
: PID process valueOUTPUT
: PID output
Integral limitation
The integral part of the PID can be limit to avoid overshoot of the output when the error is too high (When the setpoint variation is too high, or when the system have trouble to reach setpoint).
from PID_Py.PID import PID
# Initialization
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0, integralLimit = 20.0)
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
In the example above, the integral part of the PID is clamped between -20 and 20.
Manual mode
The PID can be switch in manual mode, this allow to operate output directly through manualValue
.
from PID_Py.PID import PID
# Initialization
pid = PID(kp = 0.0, ki = 0.0, kd = 0.0)
...
# Manual mode
pid.manualMode = True
pid.manualValue = 12.7
...
# PID execution (call it as fast as you can)
command = pid(processValue = feedback, setpoint = targetValue)
In the example above, command will be always equal to 12.7. The PID calculation is no longer executed. The proportionnal, integral and derivative parts still at the same value to avoid bump when switching back to automatic.
To avoid bump when switching in manual there is bumplessSwitching
attribute. This attributes keep manualValue
equal to output
.
If you disable this function you will have bump when you switch in manual mode with manualValue
different of output
. If this case you can destabilise (:heavy_exclamation_mark:) your system. Be careful
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