def mhe_init():
apm(s, b, 'clear all')
# load model and data
apm_load(s, b, 'model.apm')
csv_load(s, b, 'mhe.csv')
# configure MV / CV
apm_info(s, b, 'FV', 'Kp')
apm_info(s, b, 'FV', 'tau')
apm_info(s, b, 'FV', 'zeta')
apm_info(s, b, 'FV', 'TC_ss')
apm_info(s, b, 'MV', 'Q1')
apm_info(s, b, 'CV', 'TC')
# dynamic estimation
apm_option(s, b, 'nlc.imode', 5)
apm_option(s, b, 'nlc.solver', 3)
# tune FV
apm_option(s, b, 'Kp.dmax', 0.05)
apm_option(s, b, 'Kp.lower', 0.3)
apm_option(s, b, 'Kp.upper', 0.6)
apm_option(s, b, 'tau.dmax', 2.0)
apm_option(s, b, 'tau.lower', 40.0)
apm_option(s, b, 'tau.upper', 60.0)
apm_option(s, b, 'zeta.dmax', 0.05)
apm_option(s, b, 'zeta.lower', 1.3)
apm_option(s, b, 'zeta.upper', 1.7)
apm_option(s, b, 'TC_ss.dmax', 1.0)
apm_option(s, b, 'TC_ss.lower', 22.0)
apm_option(s, b, 'TC_ss.upper', 25.0)
# turn on FVs as degrees of freedom
apm_option(s, b, 'Kp.status', 1)
apm_option(s, b, 'tau.status', 1)
apm_option(s, b, 'zeta.status', 1)
apm_option(s, b, 'TC_ss.status', 1)
apm_option(s, b, 'Kp.fstatus', 0)
apm_option(s, b, 'tau.fstatus', 0)
apm_option(s, b, 'zeta.fstatus', 0)
apm_option(s, b, 'TC_ss.fstatus', 0)
# read Q, don't let optimize use MV
apm_option(s, b, 'Q1.status', 0)
apm_option(s, b, 'Q1.fstatus', 1)
# include CV in objective function
apm_option(s, b, 'TC.status', 1)
apm_option(s, b, 'TC.fstatus', 1)
# web-viewer option, update every second
apm_option(s, b, 'nlc.web_plot_freq', 2)
msg = 'initialization complete'
return msg
def mpc_init():
apm(s, c, 'clear all')
# load model and data
apm_load(s, c, 'model.apm')
csv_load(s, c, 'control.csv')
# configure MV / CV
apm_info(s, c, 'FV', 'Kp')
apm_info(s, c, 'FV', 'tau')
apm_info(s, c, 'FV', 'zeta')
apm_info(s, c, 'FV', 'TC_ss')
apm_info(s, c, 'MV', 'Q1')
apm_info(s, c, 'CV', 'TC')
# dynamic control
apm_option(s, c, 'nlc.imode', 6)
apm_option(s, c, 'nlc.solver', 3)
apm_option(s, c, 'nlc.hist_hor', 600)
# tune MV
# delta MV movement penalty
apm_option(s, c, 'Q1.dcost', 0.01)
# penalize voltage use (energy saQ1gs)
apm_option(s, c, 'Q1.cost', 0.01)
# limit MV movement each cycle
apm_option(s, c, 'Q1.dmax', 50)
# MV limits
apm_option(s, c, 'Q1.upper', 100)
apm_option(s, c, 'Q1.lower', 0)
# tune CV
# how fast to reach setpoint
apm_option(s, c, 'TC.tau', 10)
# trajectory type
apm_option(s, c, 'TC.tr_init', 2)
# let optimizer use MV
apm_option(s, c, 'Q1.status', 1)
# include CV in objective function
apm_option(s, c, 'TC.status', 1)
# feedback status (whether we have measurements)
apm_option(s, c, 'Q1.fstatus', 0)
apm_option(s, c, 'TC.fstatus', 1)
apm_option(s, c, 'Kp.fstatus', 1)
apm_option(s, c, 'tau.fstatus', 1)
apm_option(s, c, 'zeta.fstatus', 1)
apm_option(s, c, 'TC_ss.fstatus', 1)
# web-viewer option, update every second
apm_option(s, c, 'nlc.web_plot_freq', 1)
msg = 'initialization complete'
return msg
def mhe(T_meas,Q1):
params = np.empty(4)
# input measurements
apm_meas(s,b,'TC',T_meas)
apm_meas(s,b,'Q1',Q1)
# solve MPC
output = apm(s,b,'solve')
#print(output)
# test for successful solution
if (apm_tag(s,b,'nlc.appstatus')==1):
# retrieve the K and tau values
# option #2 retrieval (apm_tag)
Kp = apm_tag(s,b,'Kp.Newval')
tau = apm_tag(s,b,'tau.Newval')
zeta = apm_tag(s,b,'zeta.Newval')
TC_ss = apm_tag(s,b,'TC_ss.Newval')
else:
# display output for debugging
print(output)
# not successful, set default parameters
Kp = 0.359806899452
tau = 47.7311112863
zeta = 1.56206738412
TC_ss = 300.0
params[0] = Kp
params[1] = tau
params[2] = zeta
params[3] = TC_ss
return params
def mhe(T_meas, Q1):
params = np.empty(4)
# input measurements
apm_meas(s, b, 'TC', T_meas)
apm_meas(s, b, 'Q1', Q1)
# solve MPC
output = apm(s, b, 'solve')
#print(output)
# test for successful solution
if (apm_tag(s, b, 'nlc.appstatus') == 1):
# retrieve the K and tau values
# option #2 retrieval (apm_tag)
Kp = apm_tag(s, b, 'Kp.Newval')
tau = apm_tag(s, b, 'tau.Newval')
TC_ss = apm_tag(s, b, 'TC_ss.Newval')
TC = apm_tag(s, b, 'TC.Model')
else:
# display output for debugging
print(output)
# not successful, set default parameters
Kp = 0.4
tau = 160.0
TC_ss = 23.0
TC = 23.0
params[0] = Kp
params[1] = tau
params[2] = TC_ss
params[3] = TC
return params
def mpc(T_meas, params):
# input new parameter values
K = params[0]
tau = params[1]
zeta = params[2]
TC_ss = params[3]
apm_meas(s, c, 'Kp', K)
apm_meas(s, c, 'tau', tau)
apm_meas(s, c, 'zeta', zeta)
apm_meas(s, c, 'TC_ss', TC_ss)
# input measurement
apm_meas(s, c, 'TC', T_meas)
# solve MPC
output = apm(s, c, 'solve')
#print(output)
# test for successful solution
if (apm_tag(s, c, 'nlc.appstatus') == 1):
# retrieve the first Q1 value
Q1 = apm_tag(s, c, 'Q1.Newval')
else:
# display output for debugging
print(output)
# not successful, set voltage to zero
Q1 = 0
return Q1
def mpc(T1_meas,T1_sp,T2_meas,T2_sp):
# input measurement
apm_meas(s,c,'TC1',T1_meas)
apm_meas(s,c,'TC2',T2_meas)
# input setpoint with deadband +/- DT
DT = 0.1
apm_option(s,c,'TC1.sphi',T1_sp+DT)
apm_option(s,c,'TC1.splo',T1_sp-DT)
apm_option(s,c,'TC2.sphi',T2_sp+DT)
apm_option(s,c,'TC2.splo',T2_sp-DT)
# solve MPC
output = apm(s,c,'solve')
# test for successful solution
if (apm_tag(s,c,'apm.appstatus')==1):
# retrieve the Q values
Q1 = apm_tag(s,c,'Q1.Newval')
Q2 = apm_tag(s,c,'Q2.Newval')
else:
# display output for debugging
print(output)
# not successful, set voltage to zero
Q1 = 0
Q2 = 0
return [Q1,Q2]
def mpc(T_meas, T_sp):
# input measurement
apm_meas(s, c, 'T', T_meas)
# input setpoint with deadband +/- DT
DT = 0.1
apm_option(s, c, 'T.sphi', T_sp + DT)
apm_option(s, c, 'T.splo', T_sp - DT)
# solve MPC
output = apm(s, c, 'solve')
# test for successful solution
if (apm_tag(s, c, 'nlc.appstatus') == 1):
# retrieve the first Q value
Q = apm_tag(s, c, 'Q.Newval')
else:
# display output for debugging
print(output)
# not successful, set voltage to zero
Q = 0
return Q
# Basic Setup ---> from APMonitor.apm import * import json import numpy as np # apm setup server = 'http://byu.apmonitor.com' # run optimization on a provided server app = 'DEN' # intern name of the optimization apm(server, app, 'clear all') # ensure to start from scratch # <--- Basic Setup # Declare Model ---> # get model import make_splinemodel # generates model.apm version of the model in model.py from make_splinemodel import xi from make_splinemodel import eta from make_splinemodel import theta apm_load(server, app, 'model.apm' ) # loads the model.apm file # load data csv_load(server, app, 'data.csv') # <--- Declare Model # Set Optimization Options ---> # apm_options(server, app , 'options', value) see documentation for more details apm_option(server, app, 'nlc.imode', 6) # dynamic optimization # hidden input settings for i in theta: apm_info(server, app, 'MV', i ) # hidden inputs as Manipulated Variable (subject of optimization) apm_option(server, app, i + '.status' , 1) # optW activate variable for manipulation
# Import APM package
try:
from APMonitor.apm import *
except:
try:
from pip import main as pipmain
except:
from pip._internal import main as pipmain
pipmain(['install', 'APMonitor'])
from APMonitor.apm import *
# Solve optimization problem
s = 'http://127.0.0.1'
a = 'test_python'
apm(s, a, 'clear all')
apm_load(s, a, 'hs71.apm')
output = apm(s, a, 'solve')
print(output)
sol = apm_sol(s, a)
print('--- Results of the Optimization Problem ---')
print('x[1]: ' + str(sol['x[1]']))
print('x[2]: ' + str(sol['x[2]']))
print('x[3]: ' + str(sol['x[3]']))
print('x[4]: ' + str(sol['x[4]']))
# Import APM Python library "pip install APMonitor"
from APMonitor.apm import *
# Select the server
server = 'https://byu.apmonitor.com'
# Give the application a name
app = 'production'
# Clear any previous applications by that name
apm(server, app, 'clear all')
# Write the model file
fid = open('softdrink.apm', 'w')
fid.write('Variables \n')
fid.write(' x1 > 0 , < 5 ! Product 1 \n')
fid.write(' x2 > 0 , < 4 ! Product 2 \n')
fid.write(' profit \n')
fid.write(' \n')
fid.write('Equations \n')
fid.write(' ! profit function \n')
fid.write(' maximize profit \n')
fid.write(' profit = 100 * x1 + 125 * x2 \n')
fid.write(' 3 * x1 + 6 * x2 <= 30 \n')
fid.write(' 8 * x1 + 4 * x2 <= 44 \n')
fid.close()
apm_load(server, app, 'softdrink.apm')
# Solve on APM server
solver_output = apm(server, app, 'solve')
