# # Realizable PID Control
#
# The next part that needs attention is the derivative control term which is currently implemented as
#
# \begin{align}
# D & = K_D\frac{de^D}{dt} \approx K_D\frac{e^D_k - e^D_{k-1}}{t_k - t_{k-1}}
# \end{align}
#
# where is a weighted difference between the setpoing and process variable $e^D = \gamma SP - PV$.
#
# This term clearly presents problems as the control sampling period gets small. Any difference between $e^D_k$ and $e^D_{k-1}$ due to measurement noise, for example, will be magnified when divided by the small difference between $t_k$ and $t_{k-1}$.
# ## Example: PID Control with Significant Measurement Noise
#
# Here we start with the PID control algorithm that implements setpoint weighting and anti-reset windup.
# In[3]:
def PID(Kp, Ki, Kd, MV_bar=0, beta=1, gamma=0):
# initialize stored data
eD_prev = 0
t_prev = -100
P = 0
I = 0
D = 0
# initial control
MV = MV_bar
while True:
# yield MV, wait for new t, SP, PV, TR
data = yield MV
# see if a tracking data is being supplied
if len(data) < 4:
t, PV, SP = data
else:
t, PV, SP, TR = data
I = TR - MV_bar - P - D
# PID calculations
P = Kp*(beta*SP - PV)
I = I + Ki*(SP - PV)*(t - t_prev)
eD = gamma*SP - PV
D = Kd*(eD - eD_prev)/(t - t_prev)
MV = MV_bar + P + I + D
# Constrain MV to range 0 to 100 for anti-reset windup
MV = 0 if MV < 0 else 100 if MV > 100 else MV
I = MV - MV_bar - P - D
# update stored data for next iteration
eD_prev = eD
t_prev = t
# We modify the control problem by introducing a signficant degree of measurement noise.
#
# \begin{align}
# T^{meas}_{1,k} & = T_{1,k} + e_k
# \end{align}
#
# where the measurement error is a normal random variable with zero mean and a standard deviation of 2°C. For this example we've deliberately chosen a large value of $K_D = 10$ to illustrate the issue of using significant amounts of derivative control in presence of measurement noise.
# In[4]:
get_ipython().run_line_magic('matplotlib', 'inline')
from tclab import clock, setup, Historian, Plotter
import numpy as np
TCLab = setup(connected=False, speedup=10)
controller = PID(2, 0.1, 10, beta=0) # create pid control
controller.send(None) # initialize
tfinal = 600
with TCLab() as lab:
h = Historian([('SP', lambda: SP), ('PV', lambda: PV), ('MV', lambda: MV), ('Q1', lab.Q1)])
p = Plotter(h, tfinal)
T1 = lab.T1
for t in clock(tfinal, 1):
SP = T1 if t < 50 else 50 # get setpoint
PV = lab.T1 + 2*np.random.normal() # get measurement
MV = controller.send([t, PV, SP, lab.Q1()]) # compute manipulated variable
lab.Q1(MV) # apply
p.update(t) # update information display
# ## Modifying the Derivative Term
#
# To avoid problems with high frequency measurement noise and disturbances, consider an implementation of the derivative term of PID control using a first-order model
#
# \begin{align}
# \tau_f \frac{dD}{dt} + D & = K_D \frac{de^D}{dt}
# \end{align}
#
# where $e^D = \gamma SP - PV$ is the setpoint weighted error. The idea behind this model is that, for slowly varying changes, $D \sim K_d \frac{de_D}{dt}$ which provides the desired derivative control function, and for more rapidly varying changes $\tau_f \frac{dD}{dt} + D \sim K_d \frac{de_D}{dt}$ which dissipates the influence of more rapidly varying changes attributable to measurement noise.
#
# The characteristic time constant $\tau_f$ is chosen as a fraction of the derivative time constant $\tau_D$
#
# \begin{align}
# \tau_f & = \frac{\tau_D}{N} = \frac{K_D}{NK_P}
# \end{align}
#
# Rearraging for $D$ and integrating this model gives
#
# \begin{align}
# D & = \frac{K_D}{\tau_f} e^D - \frac{1}{\tau_f} \int_0^t D\ dt
# \end{align}
#
# In discrete time we define and use a backwards difference approximation to obtain
#
# \begin{align}
# S_k & = \int_0^{t_k} D\ dt \\
# & \approx \sum_{k'=1 }^{k} D_{k'} (t_{k'} - t_{k'-1}) = D_k (t_k - t_{k-1}) + S_{k-1}
# \end{align}
#
# The backwards difference approximation provides a recursion relationship that is stable for all $t_k \geq t_{k-1}$. We are thereby tracking a pair of implicit recursion relations
#
# \begin{align}
# D_k & = \frac{K_D}{\tau_f} e^D_k - \frac{1}{\tau_f} \left[ D_k(t_k - t_{k-1}) + S_{k-1} \right] \\
# \\
# S_k & = D_k (t_k - t_{k-1}) + S_{k-1}
# \end{align}
#
# Making these explicit
#
# \begin{align}
# D_k & = \frac{K_D e^D_k - S_{k-1}}{\tau_f + (t_k - t_{k-1})} \\
# \\
# S_k & = D_k (t_k - t_{k-1}) + S_{k-1}
# \end{align}
#
# Finally, using parameters for the non-interacting model of PID control
#
# \begin{align}
# D_k & = \frac{NK_P(K_De^D_k - S_{k-1})}{K_D + NK_P(t_k - t_{k-1})} \\
# \\
# S_k & = D_k (t_k - t_{k-1}) + S_{k-1}
# \end{align}
#
# This recursive calculation can be initialized with $S_0 = K_De^D_0$.
# ## Example Revisited
#
# To show the impact of this change in the implementation of the derivative term, we repeat the experiment above with the revised algorithm and $N = 1$. (A more typical value of $N$ is 5 or 10, but we've chosen a somewhat more conservative value because of the magnitude of the measurement noise present in this example, and the large value of $K_D$ chosen to demonstrate the issues.
# In[1]:
def PID(Kp, Ki, Kd, MV_bar=0, MV_min=0, MV_max=100, beta=1, gamma=0, N=10):
# initial yield and return
data = yield MV_bar
t, = data[0:3]
P = Kp*(beta*SP - PV)
MV = MV_bar + P
MV = 0 if MV < 0 else 100 if MV > 100 else MV
I = 0
D = 0
dI = 0
S = Kd*(gamma*SP - PV)
t_prev = t
while True:
# yield MV, wait for new t, SP, PV, TR
data = yield MV, P, I, D, dI
# see if a tracking data is being supplied
if len(data) < 4:
t, PV, SP = data
else:
t, PV, SP, TR = data
d = MV - TR
#I = TR - MV_bar - P - D
# PID calculations
P = Kp*(beta*SP - PV)
eD = gamma*SP - PV
D = N*Kp*(Kd*eD - S)/(Kd + N*Kp*(t - t_prev))
# conditional integration
dI = Ki*(SP - PV)*(t - t_prev)
if (MV_bar + P + I + D + dI) > MV_max:
dI = max(0, min(dI, MV_max - MV_bar - P - I - D))
if (MV_bar + P + I + D + dI) < MV_min:
dI += min(0, max(dI, MV_min - MV_bar - P - I - D))
I += dI
MV = MV_bar + P + I + D
# Clamp MV to range 0 to 100 for anti-reset windup
MV = max(MV_min, min(MV_max, MV))
# update stored data for next iteration
S = D*(t - t_prev) + S
t_prev = t
# The desired setpoint is 50°C which is acheived with a power setting of approximately 50%. We'll start out at the power setting for a period of 400 seconds, then turn on the controller.
# In[2]:
get_ipython().run_line_magic('matplotlib', 'inline')
from tclab import clock, setup, Historian, Plotter
import numpy as np
TCLab = setup(connected=False, speedup=10)
controller = PID(2, 1, 0, beta=1, N=1) # create pid control
controller.send(None) # initialize
tfinal = 600
with TCLab() as lab:
h = Historian([('SP', lambda: SP), ('PV', lambda: PV), ('MV', lambda: MV), ('Q1', lab.Q1),
('P', lambda: P), ('I', lambda: I), ('D', lambda: D), ('d', lambda: d)])
p = Plotter(h, tfinal)
T1 = lab.T1
for t in clock(tfinal, 1):
SP = T1 if t < 50 else 120 # get setpoint
PV = lab.T1 + 2*np.random.normal() # get measurement
MV, P, I, D, d = controller.send([t, PV, SP, lab.Q1()]) # compute manipulated variable
lab.Q1(MV) # apply
p.update(t) # update information display
# In[ ]:
# In[ ]:
#
# < [PID Control with Anti-Reset-Windup](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/04.04-PID-Control-with-Anti--Reset--Windup.ipynb) | [Contents](toc.ipynb) | [PID Controller Tuning](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/04.06-PID-Controller-Tuning.ipynb) >