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PID controller tuning

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I am currently optimizing a PID controller based on the gain (Kc), integral time constant (tauI), derivative time constant (tauD) and the filter derivative time constant (tauC). The problem requested is to optimize the controller in a way that the error is close to zero (average_error = 0).

The smallest error I can get is around 15 (base values) while I know I should be able to get below 1. The parameters used for this are when Kc = 1, tauI = 1, tauD = 1 and tauC = 1. This is found by using iterations, widgets, and trial and error. I'm really at a loss as to how I should code this in order to obtain the smallest possible parameters. It seems as if any change in the parameters cause a significant change in the error at this minimum value. Any help is greatly appreciated.

import sys
import control
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Pull data from CSV

file = 'profiledata.csv'
data = pd.read_csv(file,sep=',')
T_i = data.values

# Universal Variables

time_array = np.linspace(0,59,60)
perf = np.linspace(1,1,100)
average_perf = np.linspace(1,1,100)
Kc = 1
tI = 1
tD = 1
tC = 1

# Functions

for i in range(len(perf)):

 s = control.tf([1,0],[0,1])
 Gp = 1/(s**2 + s + 1)
 Gd = (s+1)/(s**2+s+1)
 Gc = Kc*(1+1/(tI*s) + tD*s/(tC*s + 1)) 
 sys_D = Gd/(1+Gp*Gc)
 _,T,_ = control.forced_response(sys_D, time_array, T_i[:,i]) 

 # Compute output based on disturbance closed loop TF sys_D

 _,Q,_ = control.forced_response(-Gc, time_array, T) # Compute input

 perf[i] = sum(abs(T) + (0.2)*abs(Q))

# Calculate the average error from the parameters 

average_perf = sum(perf)/100

print(average_perf)

The expected result is when average_perf = 0. The data file read are disturbance profiles with 100 columns and 60 rows. Is there anyway to test the optimal parameters, hold that parameter and then test the other parameters with that parameter fully optimized for the smallest error? Or am I just looking at this completely wrong. Also new to coding, so I'm not sure how to efficiently do this. The transfer functions are inputted in the for loop.

python performance controller jupyter pid

share|improve this question

edited Mar 28 at 2:27

ottomeister

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asked Mar 28 at 1:42

NickNick

111 bronze badge

add a comment | 

0

I am currently optimizing a PID controller based on the gain (Kc), integral time constant (tauI), derivative time constant (tauD) and the filter derivative time constant (tauC). The problem requested is to optimize the controller in a way that the error is close to zero (average_error = 0).

The smallest error I can get is around 15 (base values) while I know I should be able to get below 1. The parameters used for this are when Kc = 1, tauI = 1, tauD = 1 and tauC = 1. This is found by using iterations, widgets, and trial and error. I'm really at a loss as to how I should code this in order to obtain the smallest possible parameters. It seems as if any change in the parameters cause a significant change in the error at this minimum value. Any help is greatly appreciated.

import sys
import control
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Pull data from CSV

file = 'profiledata.csv'
data = pd.read_csv(file,sep=',')
T_i = data.values

# Universal Variables

time_array = np.linspace(0,59,60)
perf = np.linspace(1,1,100)
average_perf = np.linspace(1,1,100)
Kc = 1
tI = 1
tD = 1
tC = 1

# Functions

for i in range(len(perf)):

 s = control.tf([1,0],[0,1])
 Gp = 1/(s**2 + s + 1)
 Gd = (s+1)/(s**2+s+1)
 Gc = Kc*(1+1/(tI*s) + tD*s/(tC*s + 1)) 
 sys_D = Gd/(1+Gp*Gc)
 _,T,_ = control.forced_response(sys_D, time_array, T_i[:,i]) 

 # Compute output based on disturbance closed loop TF sys_D

 _,Q,_ = control.forced_response(-Gc, time_array, T) # Compute input

 perf[i] = sum(abs(T) + (0.2)*abs(Q))

# Calculate the average error from the parameters 

average_perf = sum(perf)/100

print(average_perf)

The expected result is when average_perf = 0. The data file read are disturbance profiles with 100 columns and 60 rows. Is there anyway to test the optimal parameters, hold that parameter and then test the other parameters with that parameter fully optimized for the smallest error? Or am I just looking at this completely wrong. Also new to coding, so I'm not sure how to efficiently do this. The transfer functions are inputted in the for loop.

python performance controller jupyter pid

share|improve this question

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

asked Mar 28 at 1:42

NickNick

111 bronze badge

add a comment | 

I am currently optimizing a PID controller based on the gain (Kc), integral time constant (tauI), derivative time constant (tauD) and the filter derivative time constant (tauC). The problem requested is to optimize the controller in a way that the error is close to zero (average_error = 0).

The smallest error I can get is around 15 (base values) while I know I should be able to get below 1. The parameters used for this are when Kc = 1, tauI = 1, tauD = 1 and tauC = 1. This is found by using iterations, widgets, and trial and error. I'm really at a loss as to how I should code this in order to obtain the smallest possible parameters. It seems as if any change in the parameters cause a significant change in the error at this minimum value. Any help is greatly appreciated.

import sys
import control
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Pull data from CSV

file = 'profiledata.csv'
data = pd.read_csv(file,sep=',')
T_i = data.values

# Universal Variables

time_array = np.linspace(0,59,60)
perf = np.linspace(1,1,100)
average_perf = np.linspace(1,1,100)
Kc = 1
tI = 1
tD = 1
tC = 1

# Functions

for i in range(len(perf)):

 s = control.tf([1,0],[0,1])
 Gp = 1/(s**2 + s + 1)
 Gd = (s+1)/(s**2+s+1)
 Gc = Kc*(1+1/(tI*s) + tD*s/(tC*s + 1)) 
 sys_D = Gd/(1+Gp*Gc)
 _,T,_ = control.forced_response(sys_D, time_array, T_i[:,i]) 

 # Compute output based on disturbance closed loop TF sys_D

 _,Q,_ = control.forced_response(-Gc, time_array, T) # Compute input

 perf[i] = sum(abs(T) + (0.2)*abs(Q))

# Calculate the average error from the parameters 

average_perf = sum(perf)/100

print(average_perf)

The expected result is when average_perf = 0. The data file read are disturbance profiles with 100 columns and 60 rows. Is there anyway to test the optimal parameters, hold that parameter and then test the other parameters with that parameter fully optimized for the smallest error? Or am I just looking at this completely wrong. Also new to coding, so I'm not sure how to efficiently do this. The transfer functions are inputted in the for loop.

python performance controller jupyter pid

share|improve this question

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

asked Mar 28 at 1:42

NickNick

111 bronze badge

import sys
import control
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Pull data from CSV

file = 'profiledata.csv'
data = pd.read_csv(file,sep=',')
T_i = data.values

# Universal Variables

time_array = np.linspace(0,59,60)
perf = np.linspace(1,1,100)
average_perf = np.linspace(1,1,100)
Kc = 1
tI = 1
tD = 1
tC = 1

# Functions

for i in range(len(perf)):

 s = control.tf([1,0],[0,1])
 Gp = 1/(s**2 + s + 1)
 Gd = (s+1)/(s**2+s+1)
 Gc = Kc*(1+1/(tI*s) + tD*s/(tC*s + 1)) 
 sys_D = Gd/(1+Gp*Gc)
 _,T,_ = control.forced_response(sys_D, time_array, T_i[:,i]) 

 # Compute output based on disturbance closed loop TF sys_D

 _,Q,_ = control.forced_response(-Gc, time_array, T) # Compute input

 perf[i] = sum(abs(T) + (0.2)*abs(Q))

# Calculate the average error from the parameters 

average_perf = sum(perf)/100

print(average_perf)

share|improve this question

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

asked Mar 28 at 1:42

NickNick

111 bronze badge

share|improve this question

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

asked Mar 28 at 1:42

NickNick

111 bronze badge

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

edited Mar 28 at 2:27

ottomeister

3,45722 gold badges1515 silver badges2121 bronze badges

asked Mar 28 at 1:42

NickNick

111 bronze badge

asked Mar 28 at 1:42

NickNick

111 bronze badge