mirror of
https://github.com/NickNair/Adaptive-PID-controller.git
synced 2026-02-05 09:49:42 +08:00
Added a lot of New Stuff
This commit is contained in:
Nikhil Nair
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@@ -32,6 +32,7 @@ class NeuralNetwork:
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# Description : Takes in the state vector at a given time step and computes the output vector for the next layer
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output = 1/(1 + np.exp(self.wh.dot(self.X)) )
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22
two_area.py
22
two_area.py
@@ -5,11 +5,15 @@ import math
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class TwoAreaPS:
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def __init__(self, Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3):
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def __init__(self, Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3, yt_1_,yt_2_,yt_3_):
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self.yt_1 = yt_1
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self.yt_2 = yt_2
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self.yt_3 = yt_3
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self.yt_1_a1 = yt_1
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self.yt_2_a1 = yt_2
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self.yt_3_a1 = yt_3
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self.yt_1_a2 = yt_1_
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self.yt_2_a2 = yt_2_
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self.yt_3_a2 = yt_3_
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self.Xprev = np.zeros( (7,1) )
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self.Y = np.zeros( (2,1) )
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@@ -57,8 +61,9 @@ class TwoAreaPS:
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[0,0,0,0],
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[0,1/Tg,0,-Kp/Tp] ])
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self.C = np.array( [ [ self.beta1 , 0 , 0 ,1 , 0, 0 , 0 ] ])
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# [ 0 , 0, 0, 1, self.beta2, 0, 0 ] ] )
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self.C = np.array( [ [ self.beta1 , 0 , 0 ,1 , 0, 0 , 0 ] ,
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[ 0 , 0, 0, 1, self.beta2, 0, 0 ] ,
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[ 0 , 0, 0, 1, 0, 0, 0 ] ] )
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# Calculating Discrete Coefs
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@@ -71,14 +76,13 @@ class TwoAreaPS:
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def Output(self,Ut):
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self.yt_1 , self.yt_2 , self.yt_3 = self.Y[0,0], self.yt_1, self.yt_2
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self.yt_1_a1 , self.yt_2_a1 , self.yt_3_a1 = self.Y[0,0], self.yt_1_a1, self.yt_2_a1
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self.yt_1_a2 , self.yt_2_a2 , self.yt_3_a2 = self.Y[1,0], self.yt_1_a2, self.yt_2_a2
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self.X = np.dot( self.Ad, self.Xprev ) + np.dot( self.Bd, Ut )
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self.Y = np.dot( self.C, self.Xprev)
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# print("Chooth :" , self.Y)
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self.Xprev = self.X
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if (math.isnan(self.Y[0,0])):
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276
twoarea_with_PIcontroller.py
Normal file
276
twoarea_with_PIcontroller.py
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@@ -0,0 +1,276 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy.lib.function_base import append
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import warnings
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warnings.filterwarnings("ignore")
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from two_area import *
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import neuralnetwork
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def y(yd):
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PL1 = 0
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PL2 = 0
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# rbf = RBF.RBF( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.95)
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nn1 = neuralnetwork.NeuralNetwork( aw = 0.000065, av = 0.022, au = 0.025 , gamma = 0.98)
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nn2 = neuralnetwork.NeuralNetwork( aw = 0.000065, av = 0.022, au = 0.025 , gamma = 0.98)
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# av=0.021,au = 0.025, aw = 0.0003asig = 0.01 gamma = 0.9
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# ( aw = 0.0003, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00474099
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# ( aw = 0.00008, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00037917 Settling Time ~ 70
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# ( aw = 0.00007, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00033166 Settling Time ~ 60
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# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00031556 Settling Time ~ 60
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# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.98) ----> Steady State Error : 0.00118022
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Tg = 0.08
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Tp = 20
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Tt = 0.3
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Kp = 120
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T12 = 0.545/(2*np.pi)
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a12 = -1
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R = 5
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T = dt = 1/80
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beta1 = 0.425
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beta2 = 0.425
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yt_1 = 0
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yt_2 = 0
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yt_3 = 0
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yt_1_ = 0
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yt_2_ = 0
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yt_3_ = 0
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System = TwoAreaPS( Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3 , yt_1_ ,yt_2_,yt_3_ )
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initial_states = [ yt_1, yt_2 , yt_3 , yt_1_ , yt_2_ , yt_3_]
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plot_data = {"ut1":[] , "pl1" : [] , "delF1":[] , "ut2":[] , "pl2" : [] , "delF2":[] , "time" : []}
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# KP1 = 1.32579169e-07
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# KI1 = 3.09193550e-04
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# KD1 = 1.91587817e-08
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# KP2 = 1.18316184e-07
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# KI2 = 2.70082882e-04
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# KD2 = 1.85194296e-08
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KP1 = -1.35204764e-06
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KI1 = 4.30298026e-04
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KD1 = -5.84913513e-07
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KP2 = -1.16255511e-06
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KI2 = 2.09594022e-04
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KD2 = -5.96951511e-07
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ut_1 = 0
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ut_2 = 0
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t = 200
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y=[]
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x=[]
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for i in range(0, int(t/dt) ):
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# print(System.yt_1)
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e_t_a1 = 0 - System.yt_1_a1
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del_y_a1 = System.yt_1_a1 - System.yt_2_a1
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del2_y_a1 = System.yt_1_a1 - 2*System.yt_2_a1 + System.yt_3_a1
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e_t_a2 = 0 - System.yt_1_a2
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del_y_a2 = System.yt_1_a2 - System.yt_2_a2
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del2_y_a2 = System.yt_1_a2 - 2*System.yt_2_a2 + System.yt_3_a2
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# ut_1 = ut_1 + 0.00043*e_t - 0.01*del_y - 0*del2_y
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ut_1 = ut_1 + KI1*e_t_a1 - KP1*del_y_a1 - KD1*del2_y_a1
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ut_2 = ut_2 + KI2*e_t_a2 - KP2*del_y_a2 - KD2*del2_y_a2
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plot_data["ut1"].append(ut_1)
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plot_data["ut2"].append(ut_2)
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# Load Graph 1
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# if( i*dt >= 40 ):
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# PL = 0.2
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# elif ( i*dt >= 10 ):
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# PL = 0.8
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# else:
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# PL = 0
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# Load Graph 2
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# if( i*dt <=10 ):
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# PL = 0.2
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# elif ( i*dt <= 20 ):
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# PL = 0.3
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# elif ( i*dt <= 30 ):
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# PL = 0.4
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# elif ( i*dt <= 40 ):
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# PL = 0.5
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# elif ( i*dt <= 50 ):
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# PL = 0.6
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# else:
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# PL = 0.6
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# Load Graph 3
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# if( i*dt <=10 ):
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# PL = 0.2
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# elif ( i*dt <= 20 ):
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# PL = 0.3
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# elif ( i*dt <= 30 ):
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# PL = 0.4
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# elif ( i*dt <= 40 ):
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# PL = 0.5
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# elif ( i*dt <= 50 ):
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# PL = 0.6
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# elif ( i*dt <= 60 ):
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# PL = 0.6
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# elif ( i*dt <= 70 ):
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# PL = 0.5
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# elif ( i*dt <= 80 ):
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# PL = 0.4
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# elif ( i*dt <= 90 ):
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# PL = 0.3
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# elif ( i*dt <= 100 ):
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# PL = 0.2
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# else:
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# PL = 0.1
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# Load Graph 4
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# PL = np.random.normal(0.5,0.1)
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# Load Graph 5
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if( (i*dt)%10==0 and (i*dt)!=0 ):
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PL1 = np.random.normal(0.5,0.1)
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PL2 = np.random.normal(0.5,0.1)
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# Load Graph 6
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# if ( i*dt > 50 and i*dt <130):
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# PL1 = 0.5
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# else:
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# PL1 = 0
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# if ( i*dt > 20 and i*dt <70):
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# PL2 = 0.5
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# else:
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# PL2 = 0
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plot_data["pl1"].append(PL1)
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plot_data["pl2"].append(PL2)
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Ut = [ [ut_1] , [ut_2] , [ PL1] , [PL2] ]
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System.Output(Ut)
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plot_data["delF1"].append(System.Y[0,0])
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plot_data["delF2"].append(System.Y[1,0])
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plot_data["time"].append(i*dt)
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print("The steady state error of area 1 system is : ", System.Y[0][0])
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print("The steady state error of area 2 system is : ", System.Y[1][0])
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return plot_data,initial_states
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if __name__=="__main__":
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yd = [0 for i in range(200*80) ]
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## Generate Reference array here
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plot_data,i = y(yd)
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plt.subplot(2,3,1)
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plt.plot(plot_data["time"],plot_data["pl1"], label="Reference Signal")
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plt.title( "Load vs Time")
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plt.ylabel(" Output from System ")
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plt.xlabel("Time (s)")
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plt.subplot(2,3,2)
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plt.plot(plot_data["time"],plot_data["ut1"], label="Reference Signal")
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plt.title( "Control Signal vs Time")
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plt.ylabel("Control Signal")
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plt.xlabel("Time (s)")
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plt.subplot(2,3,3)
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plt.plot(plot_data["time"],yd, label="Reference Signal")
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plt.plot(plot_data["time"],plot_data["delF1"],label ="Output")
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plt.title( " Initial States y(t-1) , y(t-2) and y(t-3) are " + str(i[0]) + ", " + str(i[1]) +" and "+ str(i[2]) )
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plt.legend()
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plt.subplot(2,3,4)
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plt.plot(plot_data["time"],plot_data["pl2"], label="Reference Signal")
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plt.title( "Load vs Time")
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plt.ylabel(" Output from System ")
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plt.xlabel("Time (s)")
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plt.subplot(2,3,5)
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plt.plot(plot_data["time"],plot_data["ut2"], label="Reference Signal")
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plt.title( "Control Signal vs Time")
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plt.ylabel("Control Signal")
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plt.xlabel("Time (s)")
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plt.subplot(2,3,6)
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plt.plot(plot_data["time"],yd, label="Reference Signal")
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plt.plot(plot_data["time"],plot_data["delF2"],label ="Output")
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plt.ylabel(" Output from System ")
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plt.xlabel("Time (s)")
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plt.show()
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@@ -2,14 +2,34 @@ import matplotlib.pyplot as plt
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import numpy as np
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from numpy.lib.function_base import append
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import warnings
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warnings.filterwarnings("ignore")
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from two_area import *
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import RBF
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def y(yd):
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rbf = RBF.RBF( aw = 0.0003, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9)
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PL1 = 0
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PL2 = 0
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# rbf = RBF.RBF( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.95)
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nn1 = RBF.RBF( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.95)
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nn2 = RBF.RBF( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.95)
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# av=0.021,au = 0.025, aw = 0.0003asig = 0.01 gamma = 0.9
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# ( aw = 0.0003, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00474099
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# ( aw = 0.00008, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00037917 Settling Time ~ 70
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# ( aw = 0.00007, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00033166 Settling Time ~ 60
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# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00031556 Settling Time ~ 60
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# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.98) ----> Steady State Error : 0.00118022
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Tg = 0.08
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@@ -18,8 +38,8 @@ def y(yd):
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Kp = 120
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T12 = 0.545/(2*np.pi)
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a12 = -1
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R = 2.4
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T = dt = 1/400
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R = 5
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T = dt = 1/80
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beta1 = 0.425
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beta2 = 0.425
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@@ -27,22 +47,38 @@ def y(yd):
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yt_2 = 0
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yt_3 = 0
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System = TwoAreaPS( Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3 )
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yt_1_ = 0
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yt_2_ = 0
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yt_3_ = 0
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System = TwoAreaPS( Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3 , yt_1_ ,yt_2_,yt_3_ )
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initial_states = [ yt_1, yt_2 , yt_3]
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initial_states = [ yt_1, yt_2 , yt_3 , yt_1_ , yt_2_ , yt_3_]
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plot_data = {"ut":[] , "pl" : [] , "delF":[] , 'KI' : [], 'KP' : [] , 'KD' : [] , "time" : []}
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plot_data = {"ut1":[] , "pl1" : [] , "delF1":[] , 'KI1' : [], 'KP1' : [] , 'KD1' : [] ,
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"ut2":[] , "pl2" : [] , "delF2":[] , 'KI2' : [], 'KP2' : [] , 'KD2' : [] , "time" : []}
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Ki = 0
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Kd = 0
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Kp = 0
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ut_1 = 0
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t = 100
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nn1.K[0] = 13
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nn1.K[1] = 1132
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nn1.K[2] = 32
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nn2.K[0] = 13
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nn2.K[1] = 1132
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nn2.K[2] = 32
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ut_1 = 0
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ut_2 = 0
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t = 200
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y=[]
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x=[]
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@@ -51,38 +87,141 @@ def y(yd):
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for i in range(0, int(t/dt) ):
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# print(System.yt_1)
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e_t = 0 - System.yt_1
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del_y = System.yt_1 - System.yt_2
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del2_y = System.yt_1 - 2*System.yt_2 + System.yt_3
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e_t_a1 = 0 - System.yt_1_a1
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del_y_a1 = System.yt_1_a1 - System.yt_2_a1
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del2_y_a1 = System.yt_1_a1 - 2*System.yt_2_a1 + System.yt_3_a1
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rbf.X[:,0] = [ e_t , -del_y , -del2_y]
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rbf.HiddenLayer()
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rbf.OutputLayer()
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nn1.X[:,0] = [ e_t_a1 , -del_y_a1 , -del2_y_a1]
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nn1.HiddenLayer()
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nn1.OutputLayer()
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e_t_a2 = 0 - System.yt_1_a2
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del_y_a2 = System.yt_1_a2 - System.yt_2_a2
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del2_y_a2 = System.yt_1_a2 - 2*System.yt_2_a2 + System.yt_3_a2
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|
||||
nn2.X[:,0] = [ e_t_a2 , -del_y_a2 , -del2_y_a2]
|
||||
nn2.HiddenLayer()
|
||||
nn2.OutputLayer()
|
||||
|
||||
|
||||
# ut_1 = ut_1 + 0.00043*e_t - 0.01*del_y - 0*del2_y
|
||||
ut_1 = ut_1 + rbf.K[1]*e_t - rbf.K[0]*del_y - rbf.K[2]*del2_y
|
||||
|
||||
plot_data["ut"].append(ut_1)
|
||||
ut_1 = ut_1 + nn1.K[1]*e_t_a1 - nn1.K[0]*del_y_a1 - nn1.K[2]*del2_y_a1
|
||||
|
||||
ut_2 = ut_2 + nn2.K[1]*e_t_a2 - nn2.K[0]*del_y_a2 - nn2.K[2]*del2_y_a2
|
||||
|
||||
|
||||
plot_data["ut1"].append(ut_1)
|
||||
plot_data["ut2"].append(ut_2)
|
||||
|
||||
# Load Graph 1
|
||||
|
||||
# if( i*dt >= 40 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt >= 10 ):
|
||||
# PL = 0.8
|
||||
# else:
|
||||
# PL = 0
|
||||
|
||||
# Load Graph 2
|
||||
|
||||
# if( i*dt <=10 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt <= 20 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 30 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 40 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 50 ):
|
||||
# PL = 0.6
|
||||
# else:
|
||||
# PL = 0.6
|
||||
|
||||
# Load Graph 3
|
||||
|
||||
# if( i*dt <=10 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt <= 20 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 30 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 40 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 50 ):
|
||||
# PL = 0.6
|
||||
# elif ( i*dt <= 60 ):
|
||||
# PL = 0.6
|
||||
# elif ( i*dt <= 70 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 80 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 90 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 100 ):
|
||||
# PL = 0.2
|
||||
# else:
|
||||
# PL = 0.1
|
||||
|
||||
# Load Graph 4
|
||||
|
||||
# PL = np.random.normal(0.5,0.1)
|
||||
|
||||
# Load Graph 5
|
||||
|
||||
if( (i*dt)%10==0 and (i*dt)!=0 ):
|
||||
PL1 = np.random.normal(0.5,0.1)
|
||||
PL2 = np.random.normal(0.5,0.1)
|
||||
|
||||
# Load Graph 6
|
||||
|
||||
# if ( i*dt > 50 and i*dt <130):
|
||||
# PL1 = 0.5
|
||||
|
||||
# else:
|
||||
# PL1 = 0
|
||||
|
||||
# if ( i*dt > 20 and i*dt <70):
|
||||
# PL2 = 0.5
|
||||
|
||||
# else:
|
||||
# PL2 = 0
|
||||
|
||||
PL = 0.2 if( i*dt >= 0.2 ) else 0
|
||||
plot_data["pl"].append(PL)
|
||||
|
||||
plot_data["pl1"].append(PL1)
|
||||
plot_data["pl2"].append(PL2)
|
||||
|
||||
Ut = [ [ut_1] , [0] , [ PL] , [0] ]
|
||||
Ut = [ [ut_1] , [ut_2] , [ PL1] , [PL2] ]
|
||||
|
||||
System.Output(Ut)
|
||||
|
||||
print(rbf.K)
|
||||
# print(rbf.K)
|
||||
|
||||
rbf.Update(0 ,System.Y[0,0] ,System.yt_1 , System.yt_2, System.yt_3 )
|
||||
nn1.Update(0 ,System.Y[0,0] ,System.yt_1_a1 , System.yt_2_a1, System.yt_3_a1 )
|
||||
nn2.Update(0 ,System.Y[1,0] ,System.yt_1_a2 , System.yt_2_a2, System.yt_3_a2 )
|
||||
|
||||
|
||||
plot_data["delF1"].append(System.Y[0,0])
|
||||
|
||||
plot_data["delF2"].append(System.Y[1,0])
|
||||
|
||||
plot_data["KI1"].append(nn1.K[1])
|
||||
plot_data["KP1"].append(nn1.K[0])
|
||||
plot_data["KD1"].append(nn1.K[2])
|
||||
|
||||
|
||||
plot_data["KI2"].append(nn2.K[1])
|
||||
plot_data["KP2"].append(nn2.K[0])
|
||||
plot_data["KD2"].append(nn2.K[2])
|
||||
|
||||
plot_data["delF"].append(System.Y[0,0])
|
||||
plot_data["time"].append(i*dt)
|
||||
plot_data["KI"].append(rbf.K[1])
|
||||
plot_data["KP"].append(rbf.K[0])
|
||||
plot_data["KD"].append(rbf.K[2])
|
||||
|
||||
print("Final Tuned Kp , Ki and Kd values for area 1 are :" , nn1.K)
|
||||
|
||||
print("The steady state error of area 1 system is : ", System.Y[0][0])
|
||||
|
||||
print("Final Tuned Kp , Ki and Kd values for area 2 are :" , nn2.K)
|
||||
|
||||
print("The steady state error of area 2 system is : ", System.Y[1][0])
|
||||
|
||||
return plot_data,initial_states
|
||||
|
||||
@@ -90,7 +229,7 @@ def y(yd):
|
||||
if __name__=="__main__":
|
||||
|
||||
|
||||
yd = [0 for i in range(100*400) ]
|
||||
yd = [0 for i in range(200*80) ]
|
||||
|
||||
|
||||
## Generate Reference array here
|
||||
@@ -99,7 +238,7 @@ if __name__=="__main__":
|
||||
|
||||
|
||||
plt.subplot(2,3,1)
|
||||
plt.plot(plot_data["time"],plot_data["pl"], label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["pl1"], label="Reference Signal")
|
||||
plt.title( "Load vs Time")
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
@@ -107,14 +246,14 @@ if __name__=="__main__":
|
||||
|
||||
|
||||
plt.subplot(2,3,2)
|
||||
plt.plot(plot_data["time"],plot_data["ut"], label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["ut1"], label="Reference Signal")
|
||||
plt.title( "Control Signal vs Time")
|
||||
plt.ylabel("Control Signal")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,3)
|
||||
plt.plot(plot_data["time"],yd, label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["delF"],label ="Output")
|
||||
plt.plot(plot_data["time"],plot_data["delF1"],label ="Output")
|
||||
|
||||
|
||||
plt.title( " Initial States y(t-1) , y(t-2) and y(t-3) are " + str(i[0]) + ", " + str(i[1]) +" and "+ str(i[2]) )
|
||||
@@ -122,20 +261,81 @@ if __name__=="__main__":
|
||||
plt.legend()
|
||||
|
||||
plt.subplot(2,3,4)
|
||||
plt.plot(plot_data["time"],plot_data["KI"], label="KI")
|
||||
|
||||
plt.plot(plot_data["time"],plot_data["pl2"], label="Reference Signal")
|
||||
plt.title( "Load vs Time")
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,5)
|
||||
plt.plot(plot_data["time"],plot_data["ut2"], label="Reference Signal")
|
||||
plt.title( "Control Signal vs Time")
|
||||
plt.ylabel("Control Signal")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,6)
|
||||
plt.plot(plot_data["time"],yd, label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["delF2"],label ="Output")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,1)
|
||||
plt.plot(plot_data["time"],plot_data["KI1"], label="KI")
|
||||
plt.title( "KI vs Time")
|
||||
plt.ylabel("KI")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,2)
|
||||
plt.plot(plot_data["time"],plot_data["KP1"], label="KP")
|
||||
plt.title( "KP vs Time")
|
||||
plt.ylabel("KP ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,3)
|
||||
plt.plot(plot_data["time"],plot_data["KD1"], label="KD")
|
||||
plt.title( "KD vs Time")
|
||||
plt.ylabel("KD")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
plt.title( " Initial States y(t-1) , y(t-2) and y(t-3) are " + str(i[0]) + ", " + str(i[1]) +" and "+ str(i[2]) )
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.subplot(2,3,4)
|
||||
plt.plot(plot_data["time"],plot_data["KI2"], label="KI")
|
||||
plt.title( "KI vs Time")
|
||||
plt.ylabel("KI")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.subplot(2,3,5)
|
||||
plt.plot(plot_data["time"],plot_data["KP"], label="KP")
|
||||
plt.plot(plot_data["time"],plot_data["KP2"], label="KP")
|
||||
plt.title( "KP vs Time")
|
||||
plt.ylabel("KP ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,6)
|
||||
plt.plot(plot_data["time"],plot_data["KD"], label="KD")
|
||||
plt.plot(plot_data["time"],plot_data["KD2"], label="KD")
|
||||
plt.title( "KD vs Time")
|
||||
plt.ylabel("KD")
|
||||
plt.xlabel("Time (s)")
|
||||
@@ -148,4 +348,3 @@ if __name__=="__main__":
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
361
twoarea_with_adaptive_pid_nn.py
Normal file
361
twoarea_with_adaptive_pid_nn.py
Normal file
@@ -0,0 +1,361 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from numpy.lib.function_base import append
|
||||
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
|
||||
from two_area import *
|
||||
|
||||
import neuralnetwork
|
||||
|
||||
|
||||
def y(yd):
|
||||
|
||||
PL1 = 0
|
||||
PL2 = 0
|
||||
|
||||
# rbf = RBF.RBF( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.95)
|
||||
|
||||
nn1 = neuralnetwork.NeuralNetwork( aw = 0.000065, av = 0.022, au = 0.025 , gamma = 0.98)
|
||||
nn2 = neuralnetwork.NeuralNetwork( aw = 0.000065, av = 0.022, au = 0.025 , gamma = 0.98)
|
||||
|
||||
# av=0.021,au = 0.025, aw = 0.0003asig = 0.01 gamma = 0.9
|
||||
|
||||
# ( aw = 0.0003, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00474099
|
||||
# ( aw = 0.00008, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00037917 Settling Time ~ 70
|
||||
# ( aw = 0.00007, av = 0.021, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00033166 Settling Time ~ 60
|
||||
# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.9) ---> Steady State Error : 0.00031556 Settling Time ~ 60
|
||||
# ( aw = 0.000065, av = 0.022, au = 0.025 , asig = 0.01, gamma = 0.98) ----> Steady State Error : 0.00118022
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Tg = 0.08
|
||||
Tp = 20
|
||||
Tt = 0.3
|
||||
Kp = 120
|
||||
T12 = 0.545/(2*np.pi)
|
||||
a12 = -1
|
||||
R = 5
|
||||
T = dt = 1/80
|
||||
beta1 = 0.425
|
||||
beta2 = 0.425
|
||||
|
||||
yt_1 = 0
|
||||
yt_2 = 0
|
||||
yt_3 = 0
|
||||
|
||||
yt_1_ = 0
|
||||
yt_2_ = 0
|
||||
yt_3_ = 0
|
||||
|
||||
System = TwoAreaPS( Tg, Tp, Tt, Kp, T12, a12, R, T, beta1, beta2, yt_1,yt_2,yt_3 , yt_1_ ,yt_2_,yt_3_ )
|
||||
|
||||
|
||||
|
||||
initial_states = [ yt_1, yt_2 , yt_3 , yt_1_ , yt_2_ , yt_3_]
|
||||
|
||||
plot_data = {"ut1":[] , "pl1" : [] , "delF1":[] , 'KI1' : [], 'KP1' : [] , 'KD1' : [] ,
|
||||
"ut2":[] , "pl2" : [] , "delF2":[] , 'KI2' : [], 'KP2' : [] , 'KD2' : [] , 'Tie Line' : [ ] ,"time" : []}
|
||||
|
||||
|
||||
Ki = 0
|
||||
Kd = 0
|
||||
Kp = 0
|
||||
|
||||
|
||||
nn1.K[0] = 13
|
||||
nn1.K[1] = 1132
|
||||
nn1.K[2] = 32
|
||||
|
||||
nn2.K[0] = 13
|
||||
nn2.K[1] = 1132
|
||||
nn2.K[2] = 32
|
||||
|
||||
|
||||
ut_1 = 0
|
||||
ut_2 = 0
|
||||
|
||||
t = 200
|
||||
|
||||
y=[]
|
||||
x=[]
|
||||
|
||||
|
||||
for i in range(0, int(t/dt) ):
|
||||
|
||||
# print(System.yt_1)
|
||||
e_t_a1 = 0 - System.yt_1_a1
|
||||
del_y_a1 = System.yt_1_a1 - System.yt_2_a1
|
||||
del2_y_a1 = System.yt_1_a1 - 2*System.yt_2_a1 + System.yt_3_a1
|
||||
|
||||
nn1.X[:,0] = [ e_t_a1 , -del_y_a1 , -del2_y_a1]
|
||||
nn1.HiddenLayer()
|
||||
nn1.OutputLayer()
|
||||
|
||||
e_t_a2 = 0 - System.yt_1_a2
|
||||
del_y_a2 = System.yt_1_a2 - System.yt_2_a2
|
||||
del2_y_a2 = System.yt_1_a2 - 2*System.yt_2_a2 + System.yt_3_a2
|
||||
|
||||
nn2.X[:,0] = [ e_t_a2 , -del_y_a2 , -del2_y_a2]
|
||||
nn2.HiddenLayer()
|
||||
nn2.OutputLayer()
|
||||
|
||||
|
||||
# ut_1 = ut_1 + 0.00043*e_t - 0.01*del_y - 0*del2_y
|
||||
ut_1 = ut_1 + nn1.K[1]*e_t_a1 - nn1.K[0]*del_y_a1 - nn1.K[2]*del2_y_a1
|
||||
|
||||
ut_2 = ut_2 + nn2.K[1]*e_t_a2 - nn2.K[0]*del_y_a2 - nn2.K[2]*del2_y_a2
|
||||
|
||||
|
||||
plot_data["ut1"].append(ut_1)
|
||||
plot_data["ut2"].append(ut_2)
|
||||
|
||||
# Load Graph 1
|
||||
|
||||
# if( i*dt >= 40 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt >= 10 ):
|
||||
# PL = 0.8
|
||||
# else:
|
||||
# PL = 0
|
||||
|
||||
# Load Graph 2
|
||||
|
||||
# if( i*dt <=10 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt <= 20 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 30 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 40 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 50 ):
|
||||
# PL = 0.6
|
||||
# else:
|
||||
# PL = 0.6
|
||||
|
||||
# Load Graph 3
|
||||
|
||||
# if( i*dt <=10 ):
|
||||
# PL = 0.2
|
||||
# elif ( i*dt <= 20 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 30 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 40 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 50 ):
|
||||
# PL = 0.6
|
||||
# elif ( i*dt <= 60 ):
|
||||
# PL = 0.6
|
||||
# elif ( i*dt <= 70 ):
|
||||
# PL = 0.5
|
||||
# elif ( i*dt <= 80 ):
|
||||
# PL = 0.4
|
||||
# elif ( i*dt <= 90 ):
|
||||
# PL = 0.3
|
||||
# elif ( i*dt <= 100 ):
|
||||
# PL = 0.2
|
||||
# else:
|
||||
# PL = 0.1
|
||||
|
||||
# Load Graph 4
|
||||
|
||||
# PL = np.random.normal(0.5,0.1)
|
||||
|
||||
# Load Graph 5
|
||||
|
||||
# if( (i*dt)%10==0 and (i*dt)!=0 ):
|
||||
# PL1 = np.random.normal(0.5,0.1)
|
||||
# PL2 = np.random.normal(0.5,0.1)
|
||||
|
||||
# Load Graph 6
|
||||
|
||||
if ( i*dt > 50 and i*dt <130):
|
||||
PL1 = 0.5
|
||||
|
||||
else:
|
||||
PL1 = 0
|
||||
|
||||
if ( i*dt > 20 and i*dt <70):
|
||||
PL2 = 0.5
|
||||
|
||||
else:
|
||||
PL2 = 0
|
||||
|
||||
|
||||
plot_data["pl1"].append(PL1)
|
||||
plot_data["pl2"].append(PL2)
|
||||
|
||||
Ut = [ [ut_1] , [ut_2] , [ PL1] , [PL2] ]
|
||||
|
||||
System.Output(Ut)
|
||||
|
||||
# print(rbf.K)
|
||||
|
||||
nn1.Update(0 ,System.Y[0,0] ,System.yt_1_a1 , System.yt_2_a1, System.yt_3_a1 )
|
||||
nn2.Update(0 ,System.Y[1,0] ,System.yt_1_a2 , System.yt_2_a2, System.yt_3_a2 )
|
||||
|
||||
|
||||
plot_data["delF1"].append(System.Y[0,0])
|
||||
|
||||
plot_data["delF2"].append(System.Y[1,0])
|
||||
|
||||
plot_data["KI1"].append(nn1.K[1])
|
||||
plot_data["KP1"].append(nn1.K[0])
|
||||
plot_data["KD1"].append(nn1.K[2])
|
||||
|
||||
|
||||
plot_data["KI2"].append(nn2.K[1])
|
||||
plot_data["KP2"].append(nn2.K[0])
|
||||
plot_data["KD2"].append(nn2.K[2])
|
||||
|
||||
plot_data["time"].append(i*dt)
|
||||
|
||||
plot_data["Tie Line"].append(System.Y[2,0])
|
||||
|
||||
print("Final Tuned Kp , Ki and Kd values for area 1 are :" , nn1.K)
|
||||
|
||||
print("The steady state error of area 1 system is : ", System.Y[0][0])
|
||||
|
||||
print("Final Tuned Kp , Ki and Kd values for area 2 are :" , nn2.K)
|
||||
|
||||
print("The steady state error of area 2 system is : ", System.Y[1][0])
|
||||
|
||||
return plot_data,initial_states
|
||||
|
||||
|
||||
if __name__=="__main__":
|
||||
|
||||
|
||||
yd = [0 for i in range(200*80) ]
|
||||
|
||||
|
||||
## Generate Reference array here
|
||||
|
||||
plot_data,i = y(yd)
|
||||
|
||||
|
||||
plt.subplot(2,3,1)
|
||||
plt.plot(plot_data["time"],plot_data["pl1"], label="Reference Signal")
|
||||
plt.title( "Load vs Time")
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,2)
|
||||
plt.plot(plot_data["time"],plot_data["ut1"], label="Reference Signal")
|
||||
plt.title( "Control Signal vs Time")
|
||||
plt.ylabel("Control Signal")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,3)
|
||||
plt.plot(plot_data["time"],yd, label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["delF1"],label ="Output")
|
||||
|
||||
|
||||
plt.title( " Initial States y(t-1) , y(t-2) and y(t-3) are " + str(i[0]) + ", " + str(i[1]) +" and "+ str(i[2]) )
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.subplot(2,3,4)
|
||||
|
||||
plt.plot(plot_data["time"],plot_data["pl2"], label="Reference Signal")
|
||||
plt.title( "Load vs Time")
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,5)
|
||||
plt.plot(plot_data["time"],plot_data["ut2"], label="Reference Signal")
|
||||
plt.title( "Control Signal vs Time")
|
||||
plt.ylabel("Control Signal")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,6)
|
||||
plt.plot(plot_data["time"],yd, label="Reference Signal")
|
||||
plt.plot(plot_data["time"],plot_data["delF2"],label ="Output")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
plt.subplot(2,3,1)
|
||||
plt.plot(plot_data["time"],plot_data["KI1"], label="KI")
|
||||
plt.title( "KI vs Time")
|
||||
plt.ylabel("KI")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,2)
|
||||
plt.plot(plot_data["time"],plot_data["KP1"], label="KP")
|
||||
plt.title( "KP vs Time")
|
||||
plt.ylabel("KP ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,3)
|
||||
plt.plot(plot_data["time"],plot_data["KD1"], label="KD")
|
||||
plt.title( "KD vs Time")
|
||||
plt.ylabel("KD")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
plt.title( " Initial States y(t-1) , y(t-2) and y(t-3) are " + str(i[0]) + ", " + str(i[1]) +" and "+ str(i[2]) )
|
||||
|
||||
plt.legend()
|
||||
|
||||
plt.subplot(2,3,4)
|
||||
plt.plot(plot_data["time"],plot_data["KI2"], label="KI")
|
||||
plt.title( "KI vs Time")
|
||||
plt.ylabel("KI")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.subplot(2,3,5)
|
||||
plt.plot(plot_data["time"],plot_data["KP2"], label="KP")
|
||||
plt.title( "KP vs Time")
|
||||
plt.ylabel("KP ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
plt.subplot(2,3,6)
|
||||
plt.plot(plot_data["time"],plot_data["KD2"], label="KD")
|
||||
plt.title( "KD vs Time")
|
||||
plt.ylabel("KD")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
|
||||
|
||||
plt.ylabel(" Output from System ")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
plt.plot(plot_data["time"],plot_data["Tie Line"])
|
||||
plt.title( "Tie Line vs Time")
|
||||
plt.ylabel("Tie Line")
|
||||
plt.xlabel("Time (s)")
|
||||
|
||||
|
||||
plt.show()
|
||||
Reference in New Issue
Block a user