"""
=======================================
Fuzzy Control Systems: Advanced Example
=======================================
The `tipping problem <./plot_tipping_problem_newapi.html>`_ is a classic,
simple example. If you're new to this, start with the `Fuzzy Control Primer
<../userguide/fuzzy_control_primer.html>`_ and move on to the tipping problem.
This example assumes you're familiar with those topics. Go on. We'll wait.
Typical Fuzzy Control System
----------------------------
Many fuzzy control systems are tasked to keep a certain variable close to a
specific value. For instance, the temperature for an industrial chemical
process might need to be kept relatively constant. In order to do this, the
system usually knows two things:
* The `error`, or deviation from the ideal value
* The way the error is changing. This is the mathematical first derivative;
we'll call it `delta`
From these two values we can construct a system which will act appropriately.
Set up the Fuzzy Control System
-------------------------------
We'll use the new control system API for this problem. It would be far too
complicated to model manually!
"""
import numpy as np
import skfuzzy.control as ctrl
# Sparse universe makes calculations faster, without sacrifice accuracy.
# Only the critical points are included here; making it higher resolution is
# unnecessary.
universe = np.linspace(-2, 2, 5)
# Create the three fuzzy variables - two inputs, one output
error = ctrl.Antecedent(universe, 'error')
delta = ctrl.Antecedent(universe, 'delta')
output = ctrl.Consequent(universe, 'output')
# Here we use the convenience `automf` to populate the fuzzy variables with
# terms. The optional kwarg `names=` lets us specify the names of our Terms.
names = ['nb', 'ns', 'ze', 'ps', 'pb']
error.automf(names=names)
delta.automf(names=names)
output.automf(names=names)
"""
Define complex rules
--------------------
This system has a complicated, fully connected set of rules defined below.
"""
rule0 = ctrl.Rule(antecedent=((error['nb'] & delta['nb']) |
(error['ns'] & delta['nb']) |
(error['nb'] & delta['ns'])),
consequent=output['nb'], label='rule nb')
rule1 = ctrl.Rule(antecedent=((error['nb'] & delta['ze']) |
(error['nb'] & delta['ps']) |
(error['ns'] & delta['ns']) |
(error['ns'] & delta['ze']) |
(error['ze'] & delta['ns']) |
(error['ze'] & delta['nb']) |
(error['ps'] & delta['nb'])),
consequent=output['ns'], label='rule ns')
rule2 = ctrl.Rule(antecedent=((error['nb'] & delta['pb']) |
(error['ns'] & delta['ps']) |
(error['ze'] & delta['ze']) |
(error['ps'] & delta['ns']) |
(error['pb'] & delta['nb'])),
consequent=output['ze'], label='rule ze')
rule3 = ctrl.Rule(antecedent=((error['ns'] & delta['pb']) |
(error['ze'] & delta['pb']) |
(error['ze'] & delta['ps']) |
(error['ps'] & delta['ps']) |
(error['ps'] & delta['ze']) |
(error['pb'] & delta['ze']) |
(error['pb'] & delta['ns'])),
consequent=output['ps'], label='rule ps')
rule4 = ctrl.Rule(antecedent=((error['ps'] & delta['pb']) |
(error['pb'] & delta['pb']) |
(error['pb'] & delta['ps'])),
consequent=output['pb'], label='rule pb')
"""
Despite the lengthy ruleset, the new fuzzy control system framework will
execute in milliseconds. Next we add these rules to a new ``ControlSystem``
and define a ``ControlSystemSimulation`` to run it.
"""
system = ctrl.ControlSystem(rules=[rule0, rule1, rule2, rule3, rule4])
# Later we intend to run this system with a 21*21 set of inputs, so we allow
# that many plus one unique runs before results are flushed.
# Subsequent runs would return in 1/8 the time!
sim = ctrl.ControlSystemSimulation(system, flush_after_run=21 * 21 + 1)
"""
View the control space
----------------------
With helpful use of Matplotlib and repeated simulations, we can observe what
the entire control system surface looks like in three dimensions!
"""
# We can simulate at higher resolution with full accuracy
upsampled = np.linspace(-2, 2, 21)
x, y = np.meshgrid(upsampled, upsampled)
z = np.zeros_like(x)
# Loop through the system 21*21 times to collect the control surface
for i in range(21):
for j in range(21):
sim.input['error'] = x[i, j]
sim.input['delta'] = y[i, j]
sim.compute()
z[i, j] = sim.output['output']
# Plot the result in pretty 3D with alpha blending
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # Required for 3D plotting
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='viridis',
linewidth=0.4, antialiased=True)
cset = ax.contourf(x, y, z, zdir='z', offset=-2.5, cmap='viridis', alpha=0.5)
cset = ax.contourf(x, y, z, zdir='x', offset=3, cmap='viridis', alpha=0.5)
cset = ax.contourf(x, y, z, zdir='y', offset=3, cmap='viridis', alpha=0.5)
ax.view_init(30, 200)
"""
.. image:: PLOT2RST.current_figure
Final thoughts
--------------
This example used a number of new, advanced techniques which may be helpful in
practical fuzzy system design:
* A highly sparse (maximally sparse) system
* Controll of Term names generated by `automf`
* A long and logically complicated ruleset, with order-of-operations respected
* Control of the cache flushing on creation of a ControlSystemSimulation,
which can be tuned as needed depending on memory constraints
* Repeated runs of a ControlSystemSimulation
* Creating and viewing a control surface in 3D.
"""