HTTP error when I run the APMonitor nlc example in python

You may have received that error if your computer was not connected to the Internet or the server was unavailable at the time you ran the test. You can install a local APM server (for Windows or Linux) to avoid any disruptions. Another option is to switch to Python gekko that uses the same underlying APM engine but can run locally with remote=False. Here is the same MPC example in Python gekko.

from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt  

m = GEKKO(remote=False)
m.time = np.linspace(0,20,41)

# Parameters
mass = 500
b = m.Param(value=50)
K = m.Param(value=0.8)

# Manipulated variable
p = m.MV(value=0, lb=0, ub=100)
p.STATUS = 1  # allow optimizer to change
p.DCOST = 0.1 # smooth out gas pedal movement
p.DMAX = 20   # slow down change of gas pedal

# Controlled Variable
v = m.CV(value=0)
v.STATUS = 1  # add the SP to the objective
m.options.CV_TYPE = 2 # squared error
v.SP = 40     # set point
v.TR_INIT = 1 # set point trajectory
v.TAU = 5     # time constant of trajectory

# Process model
m.Equation(mass*v.dt() == -v*b + K*b*p)

m.options.IMODE = 6 # control
m.solve(disp=False,GUI=True)

# get additional solution information
import json
with open(m.path+'//results.json') as f:
    results = json.load(f)

plt.figure()
plt.subplot(2,1,1)
plt.plot(m.time,p.value,'b-',label='MV Optimized')
plt.legend()
plt.ylabel('Input')
plt.subplot(2,1,2)
plt.plot(m.time,results['v1.tr'],'k-',label='Reference Trajectory')
plt.plot(m.time,v.value,'r--',label='CV Response')
plt.ylabel('Output')
plt.xlabel('Time')
plt.legend(loc='best')
plt.show()

If you'd like to get more information on either APM Python or gekko for Nonlinear or Linear Model Predictive Control, there are a series of tutorials with the Temperature Control Lab (TCLab).

Advanced Control Labs with Solutions

1. Digital Twin Model Development
   • Lab A - Single Heater Modeling
   • Lab B - Dual Heater Modeling
2. Machine Learning with Parameter and State Estimation
   • Lab C - Parameter Estimation
   • Lab D - Empirical Model Estimation
   • Lab E - Hybrid Model Estimation
3. Model Predictive Control
   • Lab F - Linear Model Predictive Control
   • Lab G - Nonlinear Model Predictive Control
   • Lab H - Moving Horizon Estimation with MPC

These exercises teach how to do first principles or empirical modeling, state estimation, and predictive control. Here is an example of combined Moving Horizon Estimation and Model Predictive Control from Lab H.