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https://github.com/chentyra/discrete-timepid
A physically achievable PID controller (discrete-time PID) with noise filter.
https://github.com/chentyra/discrete-timepid
control control-systems control-theory controller discrete-time discrete-time-regulators discrete-time-systems filter noise noise-reduction pid pid-control pid-controller pidcontroller pip python
Last synced: about 1 month ago
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A physically achievable PID controller (discrete-time PID) with noise filter.
- Host: GitHub
- URL: https://github.com/chentyra/discrete-timepid
- Owner: chentyra
- License: mit
- Created: 2023-03-29T15:22:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-03T12:57:56.000Z (7 months ago)
- Last Synced: 2024-12-22T08:34:04.470Z (about 1 month ago)
- Topics: control, control-systems, control-theory, controller, discrete-time, discrete-time-regulators, discrete-time-systems, filter, noise, noise-reduction, pid, pid-control, pid-controller, pidcontroller, pip, python
- Language: Python
- Homepage:
- Size: 46.9 KB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Discrete-Time PID
An ideal PID is based on the sum of a proportional, integrative and derivative contribution.
$u(t)= K_P(e(t) + \frac{1}{T_I}\int e(\tau)d\tau+ T_D \frac{de}{dt})$
Controller's transfer function $C(s)$ in Laplace domain is not physically achievable.
$C(s)= \frac{u(t)}{e(t)} = K_P (1+ \frac{1}{T_I \cdot s} + T_D \cdot s) = \frac{T_IT_D \cdot s^2 + T_I \cdot s + 1}{T_I \cdot s} $
Furthermore, an ideal PD amplifies measurement noise, and thus might lead to large control signals that can drive the actuator into saturation or might even cause damage. Therefore, it is necessary to filter of the derivative action in the high-frequency by defining a factor N. It usually takes on a value between 5 and 20.
$C(s)=K_P (1+ \frac{1}{T_I \cdot s} + \frac{T_D \cdot s}{1+s \cdot T_D/N})$
A Python implementation of a discrete-time PID is provided.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the discrete-time PID.
```bash
sudo pip install git+https://github.com/chentyra/Discrete-TimePID.git#egg=discretepid
```
**If the Python package was helpful to you, please give this repository a star!** :star:
## Usage
### Basic Usage
First of all, include the library:
```
from discretepid import PID
```
To create PID object, call class's constructor where:
* The first value is **proportional gain** $K_P$
* The second value is **integrative time constant** $T_I$
* The third value is **derivative time constant** $T_D$
* The fourth value is **factor** $N$
* The fifth value is **setpoint** or the value that the PID is trying to achieve
```
pid = PID(1, 0.1, 0.05, 2, setpoint=1)
```
With this definition, PID object uses a step change setpoint. The definition of an optional sixth parameter a ramp change setpoint is used by PID object.
* The ```ramping_rate``` variable defines the time interval in seconds of the ramp duration.
```
pid = PID(1, 0.1, 0.05, 2, setpoint=1, ramping_rate=1)
```
The PID compute a new ```output_value```, on the basis of an ```input_value```, calling the object created.
```
output_value= pid(input_value)
```
### An example
```
from discretepid import PID
pid = PID(0.2, 0.6, 0.02, 5, setpoint=1)
while True:
control = pid(v)
```
### Setpoint
The controller setpoint can be changed dynamically:
```
pid.setpoint = 3
```
### Ramp duration
The ramp duration can be changed dynamically:
```
pid.ramping_rate = 1
```
and can be reset calling ```reset_ramping_rate``` method:
```
pid.reset_ramping_rate()
```
> [!WARNING]
> Setting the ramping_rate variable to 0 is equivalent to using a step change setpoint.
### Sample Time
Optionally, a ```sample_time``` can be definied as last attribute of the instruction which represents the amount of time between one call to another of the updating method:
```
pid.sample_time= 0.01
```
### Output Limit
To avoid integral windup and to limit output value, attribute ```output_limits``` can be set:
```
pid.output_limits = (0, None) # Output will always be positive, but with no upper bound
```
### Switching On And Off
In order to turn off PID controller, set attribute ```auto_mode``` to False:
```
pid.auto_mode = False
```
In the same way, to turn on PID controller, set attribute ```auto_mode``` to True:
```
pid.auto_mode = True
```
When controlling the system manually, it is useful to set the value of the integral term to the value indicated by the attribute ```last_output```:
```
pid.set_auto_mode(True, last_output=1)
```
## Reset PID controller
The PID controller can be reset calling the ```reset``` method.
```
pid.reset()
```
## Other Features
The value of $K_P$, $T_I$ , $T_D$ , $N$, $setpoint$ can be seen in this way:
```
Kp, Ti, Td, N, setpoint, ramping_rate = pid.components
```
Their values can be changed individually or all at once(setpoint is optional) when the PID is running:
```
pid.Kp = 1.0
pid.tunings = (1.0, 0.3, 0.01, 10,2) #Kp,Ti,Td,N,setpoint
```
## Examples
The ```examples``` folder contains several simulations to help you understand how to use PID controller.
## License
Licensed under the [MIT][def]
[def]: https://choosealicense.com/licenses/mit/