PID control - value of process parameter based on PID result - algorithm

I'm trying to implement a PID controller following http://en.wikipedia.org/wiki/PID_controller
The mechanism I try to control works as follows:
1. I have an input variable which I can control. Typical values would be 0.5...10.
2. I have an output value which I measure daily. My goal for the output is roughly at the same range.
The two variables have strong correlation - when the process parameter goes up, the output generally goes up, but there's quite a bit of noise.
I'm following the implementation here:
http://code.activestate.com/recipes/577231-discrete-pid-controller/
Now the PID seems like it is correlated with the error term, not the measured level of output. So my guess is that I am not supposed to use it as-is for the process variable, but rather as some correction to the current value? How is that supposed to work exactly?
For example, if we take Kp=1, Ki=Kd=0, The process (input) variable is 4, the current output level is 3 and my target is a value of 2, I get the following:
error = 2-3 = -1
PID = -1
Then I should set the process variable to -1? or 4-1=3?

You need to think in terms of the PID controller correcting a manipulated variable (MV) for errors, and that you need to use an I term to get to an on-target steady-state result. The I term is how the PID retains and applies memory of the prior behavior of the system.
If you are thinking in terms of the output of the controller being changes in the MV, it is more of a 'velocity form' PID, and the memory of prior errors and behavior is integrated and accumulated in the prior MV setting.
From your example, it seems like a manipulated value of -1 is not feasible and that you would like the controller to suggest a value like 3 to get a process output (PV) of 2. For a PID controller to make use of "The process (input) variable is 4,..." (MV in my terms) Ki must be non-zero, and if the system was at steady-state, whatever was accumulated in the integral (sum_e=sum(e)) would precisely equal 4/Ki, so:
Kp= Ki = 1 ; Kd =0
error = SV - PV = 2 - 3 = -1
sum_e = sum_e + error = 4/Ki -1
MV = PID = -1(Kp) + (4/Ki -1)Ki = -1Kp + 4 - 1*Ki = -1 +4 -1 = 2
If you used a slower Ki than 1, it would smooth out the noise more and not adjust the MV so quickly:
Ki = 0.1 ;
MV = PID = -1(Kp) + (4/Ki -1)Ki = -1Kp + 4 - 1*Ki = -1 +4 -0.1 = 2.9
At steady state at target (PV = SV), sum_e * Ki should produce the steady-state MV:
PV = SV
error = SV - PV = 0
Kp * error = 0
MV = 3 = PID = 0 * Kp + Ki * sum_e
A nice way to understand the PID controller is to put units on everything and think of Kp, Ki, Kd as conversions of the process error, accumulated error*timeUnit, and rate-of-change of error/timeUnit into terms of the manipulated variable, and that the controlled system converts the controller's manipulated variable into units of output.

Related

trying to understand indicator source code/pine script/time series/comparisons/min/max

...
RsiMa = ema(Rsi, SF)
AtrRsi = abs(RsiMa[1] - RsiMa)
MaAtrRsi = ema(AtrRsi, Wilders_Period)
dar = ema(MaAtrRsi, Wilders_Period) * QQE
longband = 0.0
shortband = 0.0
DeltaFastAtrRsi = dar
RSIndex = RsiMa
newshortband = RSIndex + DeltaFastAtrRsi
newlongband = RSIndex - DeltaFastAtrRsi
// what's going on here??
longband := RSIndex[1] > longband[1] and RSIndex > longband[1] ? max(longband[1], newlongband) : newlongband
shortband := RSIndex[1] < shortband[1] and RSIndex < shortband[1] ? min(shortband[1], newshortband) : newshortband
...
Hi - I'm trying to understand what longband (and shortband) are being set to. It's very confusing because longband is initialized to a single value 0.0 but newlongband appears to be a time series. I can't make out what's going on here. I'm assuming that the RSIndex (or any time series) in the longband assignment expression is really RSIndex[0] (just like in close). After studying the manual, it doesn't look like to me that the min function or the > comparator can apply to a time series although I could be wrong. I'm also assuming that longband[1] refers to a single value - not another time series beginning at 1 instead of 0.
Very grateful for any thoughts.

how to formulate the problem of finding the optimal PID paramters in gekko?

I have defined a first order process model and would like to find the optimal PID parameters for this process. The optimization objective is to minimize the IAE ( Integral of absolute error between the setpoint and process value) for set point change over a horizon of 5 times the process time constant.
It is neither a dynamic optimization ( IMODE =6) problem , nor a pure steady state optimization problem (IMODE=3) as it involves the derivatives. How to formulate the above problem in gekko?
m = GEKKO(remote=False)
# Controller model
Kc = m.Var(1.0,lb=0.01,ub=10) # controller gain
tauI = m.Var(2.0,lb=0.01,ub=1000) # controller reset time
tauD = m.Var(1.0,lb=0.0,ub=100) # derivative constant
OP = m.Var(value=0.0,lb=0.0,ub=100) # controller output
PV = m.Var(value=0.0) # process variable
SP = 1.0 # set point
Intgl = m.Var(value=0.0) # integral of the error
err = m.Intermediate(SP-PV) # set point error
m.Equation(Intgl.dt()==err) # integral of the error
m.Equation(OP == Kc*(err + (1/tauI)*Intgl + tauD*PV.dt()))
# Process model
Kp = 2 # process gain
tauP = 10.0 # process time constant
m.Equation(tauP*PV.dt() + PV == Kp*OP)
m.Obj((SP-PV)**2) # how to define the objective to minimize the error over a horizon
m.options.IMODE=3
m.solve(disp=False)
print(str(Kc.VALUE))
print(str(tauI.VALUE))
print(str(tauD.VALUE))
print(str(m.options.OBJFCNVAL))
There is a video tutorial on simulating (00:00-17:00) and optimizing (17:00-23:41) PID tuning parameters with GEKKO. There is starting code as problem #14 in this list of tutorials.
The main points from the video are to switch to IMODE=6 and set the STATUS=1 for the parameters that should be adjusted to minimize the error: (SP-PV)**2.

Robust Standard Errors in lm() using stargazer()

I have read a lot about the pain of replicate the easy robust option from STATA to R to use robust standard errors. I replicated following approaches: StackExchange and Economic Theory Blog. They work but the problem I face is, if I want to print my results using the stargazer function (this prints the .tex code for Latex files).
Here is the illustration to my problem:
reg1 <-lm(rev~id + source + listed + country , data=data2_rev)
stargazer(reg1)
This prints the R output as .tex code (non-robust SE) If i want to use robust SE, i can do it with the sandwich package as follow:
vcov <- vcovHC(reg1, "HC1")
if I now use stargazer(vcov) only the output of the vcovHC function is printed and not the regression output itself.
With the package lmtest() it is possible to print at least the estimator, but not the observations, R2, adj. R2, Residual, Residual St.Error and the F-Statistics.
lmtest::coeftest(reg1, vcov. = sandwich::vcovHC(reg1, type = 'HC1'))
This gives the following output:
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.54923 6.85521 -0.3719 0.710611
id 0.39634 0.12376 3.2026 0.001722 **
source 1.48164 4.20183 0.3526 0.724960
country -4.00398 4.00256 -1.0004 0.319041
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
How can I add or get an output with the following parameters as well?
Residual standard error: 17.43 on 127 degrees of freedom
Multiple R-squared: 0.09676, Adjusted R-squared: 0.07543
F-statistic: 4.535 on 3 and 127 DF, p-value: 0.00469
Did anybody face the same problem and can help me out?
How can I use robust standard errors in the lm function and apply the stargazer function?
You already calculated robust standard errors, and there's an easy way to include it in the stargazeroutput:
library("sandwich")
library("plm")
library("stargazer")
data("Produc", package = "plm")
# Regression
model <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc,
index = c("state","year"),
method="pooling")
# Adjust standard errors
cov1 <- vcovHC(model, type = "HC1")
robust_se <- sqrt(diag(cov1))
# Stargazer output (with and without RSE)
stargazer(model, model, type = "text",
se = list(NULL, robust_se))
Solution found here: https://www.jakeruss.com/cheatsheets/stargazer/#robust-standard-errors-replicating-statas-robust-option
Update I'm not so much into F-Tests. People are discussing those issues, e.g. https://stats.stackexchange.com/questions/93787/f-test-formula-under-robust-standard-error
When you follow http://www3.grips.ac.jp/~yamanota/Lecture_Note_9_Heteroskedasticity
"A heteroskedasticity-robust t statistic can be obtained by dividing an OSL estimator by its robust standard error (for zero null hypotheses). The usual F-statistic, however, is invalid. Instead, we need to use the heteroskedasticity-robust Wald statistic."
and use a Wald statistic here?
This is a fairly simple solution using coeftest:
reg1 <-lm(rev~id + source + listed + country , data=data2_rev)
cl_robust <- coeftest(reg1, vcov = vcovCL, type = "HC1", cluster = ~
country)
se_robust <- cl_robust[, 2]
stargazer(reg1, reg1, cl_robust, se = list(NULL, se_robust, NULL))
Note that I only included cl_robust in the output as a verification that the results are identical.

mpi4py: Internal Error: invalid error code 409e0e (Ring ids do not match)

I am coding in python and using mpi4py to do some optimization in parallel. I am using Ordinary Least Squares, and my data is too large to fit on one processor, so I have a master process that then spawns other processes. These child processes each import a section of the data that they respectively work with throughout the optimization process.
I am using scipy.optimize.minimize for the optimization, so the child processes receive a coefficient guess from the parent process, and then report the sum of squared error (SSE) to the parent process, and then scipy.optimize.minimize goes through iterations, trying to find a minimum for the SSE. After each iteration of the minimize function, the parent broadcasts the new coefficient guesses to the child processes, who then calculate the SSE again. In the child processes, this algorithm is set up in a while loop. In the parent process, I simply call scipy.optimize.minimize.
On the part that is giving me a problem, I am doing a nested optimization, or an optimization within an optimization. The inner optimization is an OLS regression as described above, and then the outer optimization is minimizing another function that uses the coefficient of the inner optimization (the OLS regression).
So in my parent process, I have two functions that I minimize, and the second function calls on the first and does a new optimization for every iteration of the second function's optimization. The child processes have a nested while loop for those two optimizations.
Hopefully that all makes sense. If more information is needed, please let me know.
Here is the relevant code for the parent process:
comm = MPI.COMM_SELF.Spawn(sys.executable,args = ['IVQTparallelSlave_cdf.py'],maxprocs=processes)
# First stage: reg D on Z, X
def OLS(betaguess):
comm.Bcast([betaguess,MPI.DOUBLE], root=MPI.ROOT)
SSE = np.array([0],dtype='d')
comm.Reduce(None,[SSE,MPI.DOUBLE], op=MPI.SUM, root = MPI.ROOT)
comm.Bcast([np.array([1],'i'),MPI.INT], root=MPI.ROOT)
return SSE
# Here is the CDF function.
def CDF(yguess, delta_FS, tau):
# Calculate W(y) in the slave process
# Solving the Reduced form after every iteration: reg W(y) on Z, X
comm.Bcast([yguess,MPI.DOUBLE], root=MPI.ROOT)
betaguess = np.zeros(94).astype('d')
###########
# This calculates the reduced form coefficient
coeffs_RF = scipy.minimize(OLS,betaguess,method='Powell')
# This little block is to get the slave processes to stop
comm.Bcast([betaguess,MPI.DOUBLE], root=MPI.ROOT)
SSE = np.array([0],dtype='d')
comm.Reduce(None,[SSE,MPI.DOUBLE], op=MPI.SUM, root = MPI.ROOT)
cont = np.array([0],'i')
comm.Bcast([cont,MPI.INT], root=MPI.ROOT)
###########
contCDF = np.array([1],'i')
comm.Bcast([contCDF,MPI.INT], root=MPI.ROOT) # This is to keep the outer while loop going
delta_RF = coeffs_RF.x[1]
return abs(delta_RF/delta_FS - tau)
########### This one finds Y(1) ##############
betaguess = np.zeros(94).astype('d')
######### First Stage: reg D on Z, X #########
coeffs_FS = scipy.minimize(OLS,betaguess,method='Powell')
print coeffs_FS
# This little block is to get the slave processes' while loops to stop
comm.Bcast([betaguess,MPI.DOUBLE], root=MPI.ROOT)
SSE = np.array([0],dtype='d')
comm.Reduce(None,[SSE,MPI.DOUBLE], op=MPI.SUM, root = MPI.ROOT)
cont = np.array([0],'i')
comm.Bcast([cont,MPI.INT], root=MPI.ROOT)
delta_FS = coeffs_FS.x[1]
######### CDF Function #########
yguess = np.array([3340],'d')
CDF1 = lambda yguess: CDF(yguess, delta_FS, tau)
y_minned_1 = scipy.minimize(CDF1,yguess,method='Powell')
Here is the relevant code for the child processes:
#IVQTparallelSlave_cdf.py
comm = MPI.Comm.Get_parent()
.
.
.
# Importing data. The data is the matrices D, and ZX
.
.
.
########### This one finds Y(1) ##############
######### First Stage: reg D on Z, X #########
cont = np.array([1],'i')
betaguess = np.zeros(94).astype('d')
# This corresponds to 'coeffs_FS = scipy.minimize(OLS,betaguess,method='Powell')' of the parent process
while cont[0]:
comm.Bcast([betaguess,MPI.DOUBLE], root=0)
SSE = np.array(((D - np.dot(ZX,betaguess).reshape(local_n,1))**2).sum(),'d')
comm.Reduce([SSE,MPI.DOUBLE],None, op=MPI.SUM, root = 0)
comm.Bcast([cont,MPI.INT], root=0)
if rank==0: print '1st Stage OLS regression done'
######### CDF Function #########
cont = np.array([1],'i')
betaguess = np.zeros(94).astype('d')
contCDF = np.array([1],'i')
yguess = np.array([0],'d')
# This corresponds to 'y_minned_1 = spicy.minimize(CDF1,yguess,method='Powell')'
while contCDF[0]:
comm.Bcast([yguess,MPI.DOUBLE], root=0)
# This calculates the reduced form coefficient
while cont[0]:
comm.Bcast([betaguess,MPI.DOUBLE], root=0)
W = 1*(Y<=yguess)*D
SSE = np.array(((W - np.dot(ZX,betaguess).reshape(local_n,1))**2).sum(),'d')
comm.Reduce([SSE,MPI.DOUBLE],None, op=MPI.SUM, root = 0)
comm.Bcast([cont,MPI.INT], root=0)
#if rank==0: print cont
comm.Bcast([contCDF,MPI.INT], root=0)
My problem is that after one iteration through the outer minimization, it spits out the following error:
Internal Error: invalid error code 409e0e (Ring ids do not match) in MPIR_Bcast_impl:1328
Traceback (most recent call last):
File "IVQTparallelSlave_cdf.py", line 100, in <module>
if rank==0: print 'CDF iteration'
File "Comm.pyx", line 406, in mpi4py.MPI.Comm.Bcast (src/mpi4py.MPI.c:62117)
mpi4py.MPI.Exception: Other MPI error, error stack:
PMPI_Bcast(1478).....: MPI_Bcast(buf=0x2409f50, count=1, MPI_INT, root=0, comm=0x84000005) failed
MPIR_Bcast_impl(1328):
I haven't been able to find any information about this "ring id" error or how to fix it. Help would be much appreciated. Thanks!

lldb - How to display float with decimals using "type format add"

I have a variable of type float. Xcode displays it using scientific notation (i.e. 3.37626e+07). I'm trying to get it to display using dot notation (i.e. 33762616.00).
I've tried every format provided by lldb, but none displays the float using decimals. I read other posts and watched the WWDC2012 session 415 (as suggested here), but I must be too close the forest to see the trees. Any help would be greatly appreciated!
Try adding a custom data formatter in your ~/.lldbinit file for type float. e.g.
Process 13204 stopped
* thread #1: tid = 0xb6f8d, 0x0000000100000f33 a.out`main + 35 at a.c:5, stop reason = step over
#0: 0x0000000100000f33 a.out`main + 35 at a.c:5
2 int main ()
3 {
4 float f = 33762616.0;
-> 5 printf ("%f\n", f);
6 }
(lldb) p f
(float) $0 = 3.37626e+07
(lldb) type summ add -v -o "return '%f' % valobj.GetData().GetFloat(lldb.SBError(), 0)" float
(lldb) p f
(float) $1 = 33762616.000000
(lldb)
The default set of formatters provided by lldb can't do this, but dropping into Python allows you a lot of flexibility.

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