我正在嘗試使用MPC和GEKKO實現恒溫器控制。狀態變量（溫度）應該在預先指定的溫度值的上限和上限內，并在下面的代碼中。temp_lowtemp_upper兩者綁定在一天中都有所不同：每小時一個值。目標函數是使用加熱的成本。價格也隨天變化，如下所示。 是房間的外部溫度，在微分方程中起作用。TOUT_external如何實現這一點以進行優化？這是我的嘗試：from gekko import GEKKOimport numpy as npm = GEKKO(remote=False)m.time = np.linspace(0,23,24)#initialize variablesT_external = [50.,50.,50.,50.,45.,45.,45.,60.,60.,63.,64.,45.,45.,50.,52.,53.,53.,54.,54.,53.,52.,51.,50.,45.]temp_low = [55.,55.,55.,55.,55.,55.,55.,68.,68.,68.,68.,55.,55.,68.,68.,68.,68.,55.,55.,55.,55.,55.,55.,55.]temp_upper = [75.,75.,75.,75.,75.,75.,75.,70.,70.,70.,70.,75.,75.,70.,70.,70.,70.,75.,75.,75.,75.,75.,75.,75.]TOU = [0.05,0.05,0.05,0.05,0.05,0.05,0.05,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,0.05,0.05,0.05]b = m.Param(value=1.)k = m.Param(value=0.05)T_e = m.Param(value=T_external)u = m.MV(value=[0]*24, lb=[0.0]*24, ub=[1.]*24)u.STATUS = 1 # allow optimizer to change# Controlled VariableT = m.SV(value=[60]*24, lb=temp_low, ub=temp_upper)m.Equation(T.dt() == k*(T_e-T) + b*u)m.Obj(np.dot(TOU,u))m.options.IMODE = 6m.solve(debug=True)當我運行這個時，我得到：@error: Model Expression *** Error in syntax of function string: Missing operatorPosition: 4 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 ?
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TA貢獻1775條經驗 獲得超8個贊
Gekko 需要將 constaint 作為不等式表達式，其中變量與上限或下限值進行比較。如果你有，它會導致一個不可行的解決方案，因為加熱器的功率不足以將溫度保持在上限和下限。我將值更改為 以獲得可行的解決方案。TTHTLb=1.b=10
from gekko import GEKKO
import numpy as np
m = GEKKO(remote=False)
m.time = np.linspace(0,23,24)
#initialize variables
T_external = [50.,50.,50.,50.,45.,45.,45.,60.,60.,63.,\
64.,45.,45.,50.,52.,53.,53.,54.,54.,\
53.,52.,51.,50.,45.]
temp_low = [55.,55.,55.,55.,55.,55.,55.,68.,68.,68.,68.,\
55.,55.,68.,68.,68.,68.,55.,55.,55.,55.,55.,55.,55.]
temp_upper = [75.,75.,75.,75.,75.,75.,75.,70.,70.,70.,70.,75.,\
75.,70.,70.,70.,70.,75.,75.,75.,75.,75.,75.,75.]
TOU_v = [0.05,0.05,0.05,0.05,0.05,0.05,0.05,200.,200.,200.,200.,\
200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,0.05,\
0.05,0.05]
b = m.Param(value=10.)
k = m.Param(value=0.05)
T_e = m.Param(value=T_external)
TL = m.Param(value=temp_low)
TH = m.Param(value=temp_upper)
TOU = m.Param(value=TOU_v)
u = m.MV(lb=0, ub=1)
u.STATUS = 1 # allow optimizer to change
# Controlled Variable
T = m.SV(value=60)
m.Equations([T>=TL,T<=TH])
m.Equation(T.dt() == k*(T_e-T) + b*u)
m.Minimize(TOU*u)
m.options.IMODE = 6
m.solve(disp=True,debug=True)
一個可能更好的解決方案是通過將限制重新定義為錯誤來設置軟約束。您可以將誤差降至最低以保持在限制范圍內。即使它不能保持在限制范圍內，優化程序也會盡最大努力將不可行性降至最低。這還允許您同時權衡多個目標，例如在舒適度和成本之間
from gekko import GEKKO
import numpy as np
m = GEKKO(remote=False)
m.time = np.linspace(0,23,24)
#initialize variables
T_external = [50.,50.,50.,50.,45.,45.,45.,60.,60.,63.,\
64.,45.,45.,50.,52.,53.,53.,54.,54.,\
53.,52.,51.,50.,45.]
temp_low = [55.,55.,55.,55.,55.,55.,55.,68.,68.,68.,68.,\
55.,55.,68.,68.,68.,68.,55.,55.,55.,55.,55.,55.,55.]
temp_upper = [75.,75.,75.,75.,75.,75.,75.,70.,70.,70.,70.,75.,\
75.,70.,70.,70.,70.,75.,75.,75.,75.,75.,75.,75.]
TOU_v = [0.05,0.05,0.05,0.05,0.05,0.05,0.05,200.,200.,200.,200.,\
200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,0.05,\
0.05,0.05]
b = m.Param(value=10.)
k = m.Param(value=0.05)
T_e = m.Param(value=T_external)
TL = m.Param(value=temp_low)
TH = m.Param(value=temp_upper)
TOU = m.Param(value=TOU_v)
u = m.MV(lb=0, ub=1)
u.STATUS = 1 # allow optimizer to change
# Controlled Variable
T = m.SV(value=60)
# Soft constraints
eH = m.CV(value=0)
eL = m.CV(value=0)
eH.SPHI=0; eH.WSPHI=100; eH.WSPLO=0 ; eH.STATUS = 1
eL.SPLO=0; eL.WSPHI=0 ; eL.WSPLO=100; eL.STATUS = 1
m.Equations([eH==T-TH,eL==T-TL])
m.Equation(T.dt() == k*(T_e-T) + b*u)
m.Minimize(TOU*u)
m.options.IMODE = 6
m.solve(disp=True,debug=True)
import matplotlib.pyplot as plt
plt.subplot(2,1,1)
plt.plot(m.time,temp_low,'k--')
plt.plot(m.time,temp_upper,'k--')
plt.plot(m.time,T.value,'r-')
plt.ylabel('Temperature')
plt.subplot(2,1,2)
plt.step(m.time,u.value,'b:')
plt.ylabel('Heater')
plt.xlabel('Time (hr)')
plt.show()
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