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Adaptive-PID-controller/python_implementation/nonlinearsystem_with_adaptive_pid.py
2022-06-08 22:59:59 +05:30

94 lines
1.6 KiB
Python

import matplotlib.pyplot as plt
import numpy as np
import RBF
def y(yd):
rbf = RBF.RBF(0.13,0.21,0.25,0.9,0.98)
t = 1
# Initial State 1
# yt_1 = 1.1
# yt_2 = 1.1
# yt_3 = 1.1
# Initial State 2
# yt_1 = 3.23
# yt_2 = 0.32
# yt_3 = 0.23
# Initial State 3
yt_1 = 0.4
yt_2 = 0.46
yt_3 = 0.42
initial_states = [ yt_1, yt_2 , yt_3]
dt = 1/400
Ki = -0.07709546
Kd = 0.58844546
Kp = -0.01747239
ut_1 = 0
y=[]
x=[]
for i in range(0, int(t/dt) ):
e_t = yd[i] - yt_1
del_y = yt_1 - yt_2
del2_y = yt_1 - 2*yt_2 + yt_3
rbf.X[:,0] = [ e_t , -del_y , -del2_y]
rbf.HiddenLayer()
rbf.OutputLayer()
ut_1 = ut_1 + rbf.K[1]*e_t - rbf.K[0]*del_y - rbf.K[2]*del2_y
y1,y2,y3 = yt_1 , yt_2, yt_3
yt_1 , yt_2, yt_3 = yt_1*yt_2*(yt_1 + 2.5) / ( 1 + yt_1**2 + yt_2**2 )+ ut_1 +np.random.normal(0,0.01) , yt_1 , yt_2
y0 = yt_1
rbf.Update(yd[i],y0,y1,y2,y3)
y.append(yt_1)
x.append(i*dt)
return y,x,initial_states
if __name__=="__main__":
yd = [2.1 for i in range(100) ] + [3.5 for i in range(100) ] + [2 for i in range(100) ] + [3 for i in range(100) ]
## Generate Reference array here
y,x,i = y(yd)
plt.plot(x,yd, label="Reference Signal")
plt.plot(x,y,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.ylabel(" Output from System ")
plt.xlabel("Time (s)")
plt.show()