冷蔵庫を制御し、コストを最小限に抑えながら温度を一定の間隔内に保つための MPC をモデル化しています。私は GEKKO を使用してアルゴリズムをモデル化しています。
次のコードを書きました。まず、システムのセンサー データを使用してモデルを特定しました (GEKKO の関数 sysif を使用しました)。次に、入力として sysid() の結果となる ARX モデルを構築しました (GEKKO の arx 関数を使用)。
「ダミー」を書こうとしています。テストするアルゴリズムは、Pi に実装する前にローカルでテストします。
次のエラーが表示されます:
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'
これが私のコードです
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()
Th問題は次のとおりです。
T = m.CV(value=11) # Temperature will start at 11.
T 変数を再定義していますが、両方とも内部に保存されます。 11 に再初期化する必要がある場合は、T.value=11 を使用します。また、定常状態の初期化の前に eH 変数と eL 変数を追加しました。正常に実行される完全なスクリプトは次のとおりです。
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]
###########################################
#Parameter
P = m.Param(value =100) #power
TL = m.Param(value=temp_low[0])
TH = m.Param(value=temp_upper[0])
c = m.Param(value=TOU_v[0])
# 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)
# 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.
# steady state initialization
m.options.IMODE = 1
m.solve(disp=True)
TL.value = temp_low
TH.value = temp_upper
c.value = TOU_v
T.value = 11 # Temprature starts at 11
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()
コントローラーの出力は次のとおりです。
--------- APM Model Size ------------
Each time step contains
Objects : 1
Constants : 0
Variables : 9
Intermediates: 0
Connections : 2
Equations : 3
Residuals : 3
Number of state variables: 1035
Number of total equations: - 1012
Number of slack variables: - 0
------------------------
---
Degrees of freedom : 23
------------------------
----------
Dynamic Control with APOPT Solver
------------------------
----------
Iter: 1 I: 0 Tm: 0.07 NLPi: 3 Dpth: 0 Lvs: 0 Obj: 6.76E+03 Gap: 0.00E+00
Successful solution
------------------------
---------------
Solver : APOPT (v1.0)
Solution time : 8.319999999366701E-002 sec
Objective : 6763.77971670735
Successful solution
------------------------
---------------
2
1
コードを修正していただきありがとうございます。結果をプロットしようとしましたが、アルゴリズムが温度の変化に応答していないようです。 tを初期化すると温度が 11°C の場合、コントローラーは 0 に留まり、指定された制約を尊重しません。 @John Hedengren ご協力に感謝いたします。
– tt40キウイ2020 年 9 月 5 日 8:15
新しい質問を投稿していただきありがとうございます。 stackoverflow.com/questions/63753942/…
– ジョン・ヘデングレン2020 年 9 月 5 日 15:08