pct 0.0.24

Perceptual Control Theory with Python

Perceptual Control Theory

With this library you can create and run simple or complex hierarchies of perceptual control systems as well as make use of the power of the Python platform and its rich set of packages.

In the context of this library a single control system comprising a perceptual, reference, comparator and output function is called a Node. The functions therein can be configured by the user.

A hierarchy is defined by a collection of nodes.

Install

pip install pct

Import

Examples of importing the library functionality.

import pct as p

from pct.hierarchy import Hierarchy

from pct import *

How to use

Import modules from the PCT library.

from pct.nodes import PCTNode

For the purposes of this example define a world model. This would not be required if the real world is used, or a simulation such as OpenAI Gym.

def velocity_model(velocity,  force , mass):
    velocity = velocity + force / mass
    return velocity

# World value
mass = 50

Create a PCTNode, a control system unit comprising a reference, perception, comparator and output function. The default value for the reference is 1. With the history flag set, the data for each iteration is recorded for later plotting.

pctnode = PCTNode(history=True)

Call the node repeatedly to control the perception of velocity. With the verbose flag set, the control values are printed. In this case the printed values are the iteration number, the (velocity) reference, the perception, the error and the (force) output.

for i in range(40):
    print(i, end=" ")
    force = pctnode(verbose=True)
    velocity = velocity_model(pctnode.get_perception_value(), force, mass)
    pctnode.set_perception_value(velocity)

0 0.000 0.000 0.000 0.000 
1 0.000 0.000 0.000 0.000 
2 0.000 0.000 0.000 0.000 
3 0.000 0.000 0.000 0.000 
4 0.000 0.000 0.000 0.000 
5 0.000 0.000 0.000 0.000 
6 0.000 0.000 0.000 0.000 
7 0.000 0.000 0.000 0.000 
8 0.000 0.000 0.000 0.000 
9 0.000 0.000 0.000 0.000 
10 0.000 0.000 0.000 0.000 
11 0.000 0.000 0.000 0.000 
12 0.000 0.000 0.000 0.000 
13 0.000 0.000 0.000 0.000 
14 0.000 0.000 0.000 0.000 
15 0.000 0.000 0.000 0.000 
16 0.000 0.000 0.000 0.000 
17 0.000 0.000 0.000 0.000 
18 0.000 0.000 0.000 0.000 
19 0.000 0.000 0.000 0.000 
20 0.000 0.000 0.000 0.000 
21 0.000 0.000 0.000 0.000 
22 0.000 0.000 0.000 0.000 
23 0.000 0.000 0.000 0.000 
24 0.000 0.000 0.000 0.000 
25 0.000 0.000 0.000 0.000 
26 0.000 0.000 0.000 0.000 
27 0.000 0.000 0.000 0.000 
28 0.000 0.000 0.000 0.000 
29 0.000 0.000 0.000 0.000 
30 0.000 0.000 0.000 0.000 
31 0.000 0.000 0.000 0.000 
32 0.000 0.000 0.000 0.000 
33 0.000 0.000 0.000 0.000 
34 0.000 0.000 0.000 0.000 
35 0.000 0.000 0.000 0.000 
36 0.000 0.000 0.000 0.000 
37 0.000 0.000 0.000 0.000 
38 0.000 0.000 0.000 0.000 
39 0.000 0.000 0.000 0.000 

Using the plotly library plot the data. The graph shows the perception being controlled to match the reference value.

import plotly.graph_objects as go
fig = go.Figure(layout_title_text="Velocity Goal")
fig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['constant'], name="ref"))
fig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['variable'], name="perc"))

This following code is only for the purposes of displaying image of the graph generated by the above code.

from IPython.display import Image

Image(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')

This shows a very basic example of the use of the PCT library. For more advanced functionality see the API documentation at https://perceptualrobots.github.io/pct/.