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guadaloop_lev_control/MAGLEV_DIGITALTWIN_PYTHON/controller.py

125 lines
3.9 KiB
Python

"""
Decentralized PID controller for maglev system
Ported from decentralizedPIDcontroller.m
"""
import numpy as np
class DecentralizedPIDController:
"""
Decentralized PID controller for quadrotor/maglev control.
Controls altitude, roll, and pitch using gap sensor feedback.
"""
def __init__(self):
# Persistent variables (maintain state between calls)
self.preverror = np.zeros(4)
self.cumerror = np.zeros(4)
def reset(self):
"""Reset persistent variables"""
self.preverror = np.zeros(4)
self.cumerror = np.zeros(4)
def control(self, R, S, P):
"""
Compute control voltages for each yoke.
Parameters
----------
R : dict
Reference structure with elements:
- rIstark : 3-element array of desired CM position at time tk in I frame (meters)
- vIstark : 3-element array of desired CM velocity (meters/sec)
- aIstark : 3-element array of desired CM acceleration (meters/sec^2)
S : dict
State structure with element:
- statek : dict containing:
- rI : 3-element position in I frame (meters)
- RBI : 3x3 or 9-element direction cosine matrix
- vI : 3-element velocity (meters/sec)
- omegaB : 3-element angular rate vector in body frame (rad/sec)
P : dict
Parameters structure with elements:
- quadParams : QuadParams object
- constants : Constants object
Returns
-------
ea : ndarray, shape (4,)
4-element vector with voltages applied to each yoke
"""
# Extract current state
zcg = S['statek']['rI'][2] # z-component of CG position
rl = P['quadParams'].sensor_loc
# Reshape RBI if needed
RBI = S['statek']['RBI']
if RBI.shape == (9,):
RBI = RBI.reshape(3, 3)
# Calculate gaps at sensor locations
gaps = np.abs(zcg) - np.array([0, 0, 1]) @ RBI.T @ rl
gaps = gaps.flatten()
# Controller gains
kp = 10000
ki = 0
kd = 50000
# Reference z position
refz = R['rIstark'][2]
meangap = np.mean(gaps)
# Transform gaps using corner-based sensing
# gaps indices: 0=front, 1=right, 2=back, 3=left
gaps_transformed = np.array([
gaps[0] + gaps[3] - meangap, # gaps(1)+gaps(4)-meangap in MATLAB
gaps[0] + gaps[1] - meangap, # gaps(1)+gaps(2)-meangap
gaps[2] + gaps[1] - meangap, # gaps(3)+gaps(2)-meangap
gaps[2] + gaps[3] - meangap # gaps(3)+gaps(4)-meangap
])
# Calculate error for each yoke
err = -refz - gaps_transformed
derr = err - self.preverror
self.preverror = err
self.cumerror = self.cumerror + err
# Calculate desired voltages
eadesired = kp * err + derr * kd + ki * self.cumerror
# Apply saturation
s = np.sign(eadesired)
maxea = P['quadParams'].maxVoltage * np.ones(4)
ea = s * np.minimum(np.abs(eadesired), maxea)
return ea
def decentralized_pid_controller(R, S, P, controller=None):
"""
Wrapper function to maintain compatibility with MATLAB-style function calls.
Parameters
----------
R, S, P : dict
See DecentralizedPIDController.control() for details
controller : DecentralizedPIDController, optional
Controller instance to use. If None, creates a new one (loses state)
Returns
-------
ea : ndarray, shape (4,)
4-element vector with voltages applied to each yoke
"""
if controller is None:
controller = DecentralizedPIDController()
return controller.control(R, S, P)