Part 4: The Controller Code (Python)
The Brain. The actual Python code that runs the feedback loop.
import numpy as np
from safetymonitor import SafetyMonitor
class NanoController:
"""
A controller class for the NanoCERN healing instrument.
It takes in raw sensor data, calculates the healing state vector SH,
decides on the best control action to minimize the potential V,
and maps abstract controls to concrete hardware commands.
"""
def _init__(self):
# Define the parameters of the potential V
self.kscar = 1.0 # Scar well depth
self.kregen = 1.0 # Regen well depth
self.psibarrier = 1.0 # Activation energy barrier
# State Memory for Hysteresis
self.boosting = False
# Safety Monitor
self.safety = SafetyMonitor()
# Define the sensor and actuator mappings
self.sensormap = {
'membranepotential': 0,
'conductivity': 1,
'chemicalgradient': 2,
'stiffness': 3,
'thermalnoise': 4,
'permittivity': 5,
'ioniccurrent': 6,
'boundarycontinuity': 7,
'entropyproduction': 8,
'mechanicaltension': 9
}
self.actuatormap = {
'helmholtzcoil': 0,
'electrodes': 1,
'piezoelectricstretchers': 2
}
def step(self, sensordata):
"""
Takes in raw sensor readings and returns control signals.
Parameters:
sensordata (list): A list of raw sensor readings.
Returns:
controlsignals (list): A list of control signals for the actuators.
"""
# Calculate the healing state vector SH
SH = self.calculateSH(sensordata)
# Calculate the potential V
V = self.calculatepotential(SH)
# Decide on the best control action
action = self.decideaction(SH)
# Map abstract controls to concrete hardware commands
controlsignals = self.mapactiontocontrol(action)
return controlsignals
def calculateSH(self, sensordata):
"""
Calculates the healing state vector SH from raw sensor readings.
Parameters:
sensordata (list): A list of raw sensor readings.
Returns:
SH (list): The healing state vector.
"""
SH = [0.0] * 10 # Initialize SH with zeros
# Map sensor readings to SH components
for i, component in enumerate(self.sensormap.keys()):
SH[i] = sensordata[self.sensormap[component]]
return SH
def calculatepotential(self, SH):
"""
Calculates the potential V based on the Attractor Model.
Parameters:
SH (list): The healing state vector.
Returns:
V (float): The potential V.
"""
# Calculate the scar and regen well energies
Escar = self.kscar * (SH[0] - 1.0)**2
Eregen = self.kregen * (SH[0] - 2.0)**2
# Calculate the activation energy barrier
psi = self.psibarrier * np.exp(-(SH[1] - 1.0)**2)
# Calculate the total potential V
V = Escar + Eregen + psi
return V
def decideaction(self, SH):
"""
Decides on the best control action to minimize the potential V.
Uses Hysteresis to prevent premature relaxation.
Parameters:
SH (list): The healing state vector.
Returns:
action (str): The best control action.
"""
# Hysteresis Logic
# Trigger Point: < 1.5 (Enter Scar Zone)
# Release Point: > 1.8 (Deep in Regen Zone)
phim = SH[0]
if phim < 1.5:
self.boosting = True
elif phim > 1.8:
self.boosting = False
# Decide on the best control action
if self.boosting:
action = 'increasecoherence'
else:
action = 'maintaincoherence'
return action
def mapactiontocontrol(self, action):
"""
Maps abstract controls to concrete hardware commands.
Parameters:
action (str): The abstract control action.
Returns:
controlsignals (list): The concrete hardware commands.
"""
controlsignals = [0.0] * 3 # Initialize control signals with zeros
# Map abstract controls to concrete hardware commands
if action == 'increasecoherence':
controlsignals[self.actuatormap['helmholtzcoil']] = 150.0 # Set Helmholtz coil to 150Hz
elif action == 'maintaincoherence':
controlsignals[self.actuatormap['helmholtzcoil']] = 100.0 # Set Helmholtz coil to 100Hz
return controlsignals
# Example usage:
controller = NanoController()
sensordata = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
controlsignals = controller.step(sensordata)
print(controlsignals)