def get_hall_sensor_value_z(self, zReading, sensor_idx):
zReading_Sensor = zReading[self.hall_sensor_reading_repetition *
sensor_idx:self.
hall_sensor_reading_repetition *
(sensor_idx + 1)]
# Kalman filter for hall sensor noise reduction
zReading_Sensor_Average = Kalman_Filter(
self.hall_sensor_reading_repetition, zReading_Sensor)
return zReading_Sensor_Average
print("Start of iteration ",t,"/",loop_iteration_total)
# read all hall sensor values in Gauss
if t > 10:
n_reading = 1 # increase readings for higher precision at setpoint
nmrObj.igbtSenReadingMulti(sen_address, n_reading, enable_message)
zReading = parse_csv_returnZreading(data_folder, 'Current_Reading.csv')
for n in range(0,magnets_num):
# get hall sensor value for magnet n
zReading_Sensor = zReading[n_reading* n: n_reading*(n+1)]
# kalman filter for hall sensor noise reduction
zReading_Sensor_Average = Kalman_Filter(n_reading, zReading_Sensor)
# compute new output
u = controllers[n].update(zReading_Sensor_Average)
# assemble actuation vector
if u >= 0:
plength[forward_direction_addr[n]] = abs(np.int(u))
if u < 0:
plength[reverse_direction_addr[n]] = abs(np.int(u))
print("\tHall Sensor [",sen_address[n],"]","=",zReading_Sensor_Average,"Gauss","==> Pulse:",round(u,2),"us")
nmrObj.igbtPulseMagnetControl(plength, pspac, pulse_reptition) # actuate magnets
print("\t plength : {}".format(plength))
# number of readings
n_reading = 5
try:
os.remove(data_folder + '/Current_Reading.csv') # delete current_reading.csv every time
except:
print("file does not exist")
# read hall sensor value in Tesla
nmrObj.igbtSenReading(sen_address, n_reading)
zReading = parse_csv_returnZreading(data_folder, 'Current_Reading.csv') # in Gauss
#os.remove(data_folder + '/Current_Reading.csv') # delete current_reading.csv every time
time.sleep(1.1)
# kalman filter
ZReading_Average = Kalman_Filter(n_reading, zReading)
y = ZReading_Average
print("\n")
print("\tCurrent hall reading is : {}".format(y))
print("\tSensor Address is : {}".format(sen_address))
print("\n")
nmrObj.igbtPulse(plength, pspac, iter)
try:
os.remove(data_folder + '/Current_Reading.csv') # delete current_reading.csv every time
except:
print("file does not exist")
# read hall sensor value in Tesla
# nmrObj.igbtSenReading(sen_address, n_reading)
n_iter = 1 # number of iteration for hall reading
for t in range(0, 3): # control algorithm main loop
r = setpoint # read target magnetization value, e.g. from GUI
# --------------------------------------
# read hall sensor value in Tesla
nmrObj.igbtSenReading(sen_address, n_iter)
zReading = parse_csv_returnZreading(data_folder,
'Current_Reading.csv') # in Gauss
time.sleep(0.1)
# average
#ZReading_Average = sum(zReading)/len(zReading)/10 # transfer to mT
# kalman filter
ZReading_Average = Kalman_Filter(n_iter, zReading)
y = ZReading_Average
print("\n")
print("\tCurrent hall reading is : {}".format(y))
print("\n")
# --------------------------------------
#print(y)
e = r - y
#print(e) # calculate setpoint tracking error
P = Kp * (b * r - y) # compute proportional part
u = P + I # compute output
if u > maxDutyCycle: # anti-windup strategy
u = maxDutyCycle
#plength_forward[0] = u
def pulseMagnet(param_dict, setpoints, pulse_on, abs_pulse, loop_iteration_total=10, save_data=True):
magnets_num = int(param_dict['number_of_magnets'])
Kp = param_dict['Kp_init']
Ki = param_dict['Ki_init']
beta = param_dict['Beta']
min_pulse_length, max_pulse_length = -abs_pulse, abs_pulse
bh_factor = np.zeros(magnets_num)
v1 = 1
v2 = 1.5
v3 = 3
bh_factor[0] = v1
bh_factor[1] = v2
bh_factor[2] = v1
bh_factor[3] = v2
bh_factor[4] = v3
bh_factor[5] = v2
bh_factor[6] = v1
bh_factor[7] = v2
bh_factor[8] = v1
for n in range(0, magnets_num):
Kp_weight = Kp * bh_factor[n]
Ki_weight = Ki * bh_factor[n]
controllers.append(FeedbackController(Kp_weight, Ki_weight, Kd, beta,
setpoint=setpoints[n],
output_limits=(
min_pulse_length, max_pulse_length),
setpoint_weighting=False,
proportional_on_input=False,
output_enabled=pulse_on[n],
measurement_smoothing_enabled=False,
measurement_smoothing_start=10,
measurement_smoothing_past=3))
# ------------------------------------------------------------------------------
for t in range(0, loop_iteration_total):
plength = np.zeros(num_channel)
print("Start of iteration {}".format(t))
# read hall sensor value in Tesla
nmrObj.igbtSenReadingMulti(sen_address, n_reading, enable_message)
zReading = parse_csv_returnZreading(
data_folder, 'Current_Reading.csv') # in Gauss
for n in range(0, magnets_num):
# read hall sensor value in Tesla
# nmrObj.igbtSenReading(sen_address[n], n_reading)
zReading_Sensor = zReading[n_reading * n: n_reading*(n+1)]
# kalman filter
ZReading_Average = Kalman_Filter(n_reading, zReading_Sensor)
y = ZReading_Average
# average past measurements to reduce sensor noise
t_past = 3
if t > t_past:
temp = np.mean(controllers[n].input_data[t-t_past:t])
past_average = np.mean([temp, y])
y = round(past_average, 2)
# print("\n")
print("\tCurrent hall reading is : {}".format(y))
print("\tSensor Address is : {}".format(sen_address[n]))
# print("\n")
# ---------------------------
# compute new ouput. Save value in array U
u = controllers[n].update(y)
# determine by the pin assignment to the channels
u = u * (-1)
if u >= 0:
plength[forward_direction_addr[n]] = abs(
np.int(u)) # actuate magnets
if u < 0:
plength[reverse_direction_addr[n]] = abs(np.int(u))
# delete current_reading.csv every time
os.remove(data_folder + '/Current_Reading.csv')
print("\t plength : {}".format(plength))
nmrObj.igbtPulseMagnetControl(plength, pspac, pulse_reptition)
# save results as MAT file
if save_data == True:
experiment_data = {}
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y_%m_%d %H_%M_%S')
for n in range(0, magnets_num):
experiment_data.update({'magnet_'+str(n): {
'control_tunings': controllers[n].tunings,
'beta': controllers[n].beta,
'setpoint_weighting': controllers[n].setpoint_weighting,
'proportional_on_input': controllers[n].proportional_on_input,
'Iteration': controllers[n].time_total,
'control_pulse': controllers[n].output_data,
'magnetization': controllers[n].input_data,
'error': controllers[n].error_data,
'setpoint': controllers[n].setpoint,
'integral': controllers[n].integral_data
}})
sio.savemat(logging_file_name+st+'.mat', experiment_data)
def pulseMagnet(param_dict,
setpoints,
pulse_on,
abs_pulse=50,
loop_iteration_total=10,
save_data=True):
magnets_num = int(param_dict['number_of_magnets'])
Kp = param_dict['Kp_init']
Ki = param_dict['Ki_init']
beta = param_dict['Beta']
min_pulse_length, max_pulse_length = -abs_pulse, abs_pulse
magnets = []
controllers = []
for n in range(0, magnets_num):
controllers.append(
FeedbackController(Kp,
Ki,
Kd,
beta,
setpoint=setpoints,
output_limits=(min_pulse_length,
max_pulse_length),
setpoint_weighting=False,
proportional_on_input=False,
output_enabled=pulse_on[n]))
# ------------------------------------------------------------------------------
for t in range(0, loop_iteration_total):
plength = np.zeros(num_channel)
print("Start of iteration ", t, "/", loop_iteration_total)
# read all hall sensor values in Gauss
if t > 10:
n_reading = 1 # increase readings for higher precision at setpoint
nmrObj.igbtSenReadingMulti(sen_address, n_reading, enable_message)
zReading = parse_csv_returnZreading(data_folder, 'Current_Reading.csv')
for n in range(0, magnets_num):
# get hall sensor value for magnet n
zReading_Sensor = zReading[n_reading * n:n_reading * (n + 1)]
# kalman filter for hall sensor noise reduction
zReading_Sensor_Average = Kalman_Filter(n_reading, zReading_Sensor)
# compute new output
u = controllers[n].update(zReading_Sensor_Average)
# assemble actuation vector
if u >= 0:
plength[forward_direction_addr[n]] = abs(np.int(u))
if u < 0:
plength[reverse_direction_addr[n]] = abs(np.int(u))
print("\tHall Sensor [", sen_address[n], "]",
"=", zReading_Sensor_Average, "Gauss", "==> Pulse:",
round(u, 2), "us")
nmrObj.igbtPulseMagnetControl(plength, pspac,
pulse_reptition) # actuate magnets
print("\t plength : {}".format(plength))
# save results as MAT file
if save_data == True:
experiment_data = {}
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y_%m_%d %H_%M_%S')
for n in range(0, magnets_num):
experiment_data.update({
'magnet_' + str(n): {
'control_tunings': controllers[n].tunings,
'beta': controllers[n].beta,
'setpoint_weighting': controllers[n].setpoint_weighting,
'proportional_on_input':
controllers[n].proportional_on_input,
'Iteration': controllers[n].time_total,
'control_pulse': controllers[n].output_data,
'magnetization': controllers[n].input_data,
'error': controllers[n].error_data,
'setpoint': controllers[n].setpoint,
'integral': controllers[n].integral_data
}
})
sio.savemat(logging_file_name + st + '.mat', experiment_data)