MPC con modello ARX utilizzando Gekko

Sep 04 2020

Sto modellando un MPC per controllare un frigorifero e mantenere la temperatura entro un dato intervallo riducendo al minimo i costi. Sto usando GEKKO per modellare il mio algoritmo.

Ho scritto il codice seguente. Per prima cosa, ho identificato il mio modello utilizzando i dati del sensore dal mio sistema (ho usato la funzione sysif di GEKKO). Quindi ho costruito un modello ARX (usando la funzione arx in GEKKO) che diventa il risultato di sysid () come input.

Sto cercando di scrivere un algoritmo "fittizio" da testare localmente prima di implementarlo in un Pi.

Ottengo il seguente errore:

KeyError                                  Traceback (most recent call last)
<ipython-input-13-108148376700> in <module>
    107 #Solve the optimization problem.
    108 
--> 109 m.solve()

~/opt/anaconda3/lib/python3.8/site-packages/gekko/gekko.py in solve(self, disp, debug, GUI, **kwargs)
   2214         if timing == True:
   2215             t = time.time()
-> 2216         self.load_JSON()
   2217         if timing == True:
   2218             print('load JSON', time.time() - t)

~/opt/anaconda3/lib/python3.8/site-packages/gekko/gk_post_solve.py in load_JSON(self)
     48                             vp.__dict__[o] = dpred
     49                 else: #everything besides value, dpred and pred
---> 50                     vp.__dict__[o] = data[vp.name][o]
     51     for vp in self._variables:
     52         if vp.type != None: #(FV/MV/SV/CV) not Param or Var

KeyError: 'int_p6'

E questo è il mio codice

from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
m = GEKKO(remote = True)


#initialize variables

#Room Temprature:
T_external = [23,23,23,23,23.5,23.5,23.4,23.5,23.9,23.7,\
              23,23.9,23.9,23.4,23.9,24,23.6,23.7,23.8,\
              23,23,23,23,23]

# Temprature Lower Limit:
temp_low = 10*np.ones(24)

# Temprature Upper Limit:
temp_upper = 12*np.ones(24)

#Hourly Energy prices:
TOU_v = [39.09,34.93,38.39,40.46,40.57,43.93,25,11,9,24,51.28,45.22,45.72,\
            36,35.03,10,12,13,32.81,42.55,8,29.58,29.52,29.52]

###########################################
#System Identification:

#Time 
t = np.linspace(0,10,117)
#State of the Fridge
ud = np.append(np.zeros(78) ,np.ones(39),0)
#Temprature Data
y = [14.600000000000001,14.600000000000001,14.700000000000001,14.700000000000001,14.700000000000001,\
     14.700000000000001,14.700000000000001,14.700000000000001,14.700000000000001,14.700000000000001,\
     14.700000000000001,14.700000000000001,14.700000000000001,14.8,14.8,14.8,14.8,14.8,14.8,14.8,14.8,\
    14.8,14.8,14.9,14.9,14.9,14.9,14.9,14.9,14.9,15,15,15,15,15,15,15,15,15,15,15,15,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,\
    15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,15.100000000000001,
    15,15,15,15,15,15,15,15,15,15,14.9,14.9,14.9,14.9,14.8,14.9,14.8,14.8,14.8,14.8,14.8,14.8,\
    14.8,14.700000000000001,14.8,14.700000000000001,14.700000000000001,14.700000000000001,\
    14.700000000000001,14.700000000000001,14.700000000000001,14.700000000000001,\
    14.700000000000001,14.600000000000001,14.600000000000001,14.600000000000001,\
    14.600000000000001,14.600000000000001,14.60]

na = 1 # output coefficients
nb = 1 # input coefficients
print('Identification')
yp,p,K = m.sysid(t,ud,y,na,nb,objf=10000,scale=False,diaglevel=1)
#create control ARX model:

y = m.Array(m.CV,1)
uc = m.Array(m.MV,1)
m.arx(p,y,uc)
# rename CVs
T= y[0]

# rename MVs
uc = uc[0]

# steady state initialization
m.options.IMODE = 1
m.solve(disp=True)

###########################################

#Parameter
P = m.Param(value =100) #power
TL = m.Param(value=temp_low) 
TH = m.Param(value=temp_upper)
c = m.Param(value=TOU_v)
# Manipilated variable:

u = m.MV(lb=0, ub=1, integer=True)
u.STATUS = 1  # allow optimizer to change the variable to attein the optimum.

# Controlled Variable (Affected with changes in the manipulated variable)

T = m.CV(value=11) # Temprature will start at 11.

# Soft constraints on temprature.

eH = m.CV(value=0)
eL = m.CV(value=0)

eH.SPHI=0       #Set point high for linear error model.
eH.WSPHI=100    #Objective function weight on upper set point for linear error model.
eH.WSPLO=0      # Objective function weight on lower set point for linear error model
eH.STATUS =1    # eH : Error is considered in the objective function.
eL.SPLO=0
eL.WSPHI=0
eL.WSPLO=100 
eL.STATUS = 1   
#Linear error (Deviation from the limits)
m.Equations([eH==T-TH,eL==T-TL])

#Objective : minimize the costs.

m.Minimize(c*P*u)

#Optimizer Options.

m.options.IMODE = 6 # MPC mode in Gekko.
m.options.NODES = 2  # Collocation nodes.
m.options.SOLVER = 1 # APOT solver for mixed integer linear programming.
m.time = np.linspace(0,23,24)

#Solve the optimization problem.

m.solve()

Risposte

2 JohnHedengren Sep 04 2020 at 03:41

Il problema è con:

T = m.CV(value=11) # Temperature will start at 11.

Stai ridefinendo la Tvariabile ma memorizza entrambe internamente. Se è necessario reinizializzare per 11poi utilizzare T.value=11. Inoltre, ho aggiunto le variabili eHe eLprima dell'inizializzazione dello stato stazionario. Ecco uno script completo che viene eseguito correttamente.