## Model Predictive Control

## Main.ControlTypes History

Show minor edits - Show changes to output

Added lines 4-23:

Model predictive control (MPC) is a type of control algorithm that is used to control systems with dynamics. It is a model-based method that uses a predictive model of the system to compute control actions that optimize a performance criterion over a finite horizon.

The basic idea of MPC is to optimize a control policy that minimizes the difference between the desired reference signal and the output of the system, subject to constraints on the control inputs and the states of the system. This optimization problem can be written as follows:

{$\min_{u_k, ..., u_{k+N-1}} \sum_{i=k}^{k+N-1} ||r_i - y_i||^2 + \sum_{i=k}^{k+N-1} ||u_i - u_{i-1}||^2$}

subject to:

{$x_{k+1} = f(x_k, u_k)$}

{$g_j(x_i, u_i) \leq 0 \quad (j=1,2,...,m)$}

where {`u_k,\ldots, u_{k+N-1}`} are the control inputs at each time step in the horizon, ''r'_i_''' is the reference signal at time ''i'', ''y'_i_''' is the output of the system at time ''i'', ''f(x'_k_', u'_k_')'' is the model prediction of the state at time ''k+1'' given the state at time ''k'' and the control input at time ''k'', ''x'_k+1_''' is the state at time ''k+1'', and ''g'_j_'(x'_i_', u'_i_')'' are the constraints on the states and control inputs at time ''i''.

The optimization problem is solved using an optimization algorithm, such as the interior point method, at each time step. The solution of the optimization problem gives the optimal control inputs for the current time step and the next few time steps in the horizon. The control inputs for the current time step are then applied to the system, and the horizon is shifted forward by one time step. The process is then repeated at the next time step.

MPC has several advantages compared to other control algorithms. It allows the control of systems with constraints on the control inputs and states, it can handle a wide range of performance criteria, and it can handle systems with time-varying dynamics. It is widely used in industries such as chemical process control, automotive control, and power systems.

----

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plt.plot(m.time,u.value,'r-',~~LineWidth~~=2)

to:

plt.plot(m.time,u.value,'r-',lw=2)

Changed line 103 from:

plt.plot(m.time,v.value,'b--',~~LineWidth~~=2)

to:

plt.plot(m.time,v.value,'b--',lw=2)

Changed line 107 from:

plt.plot(m.time,y.value,'g:',~~LineWidth~~=2)

to:

plt.plot(m.time,y.value,'g:',lw=2)

Changed lines 111-112 from:

plt.plot(m.time,theta.value,'m-',~~LineWidth~~=2)

plt.plot(m.time,q.value,'k.-',~~LineWidth~~=2)

plt.plot(m.time,q.value,'k.-',

to:

plt.plot(m.time,theta.value,'m-',lw=2)

plt.plot(m.time,q.value,'k.-',lw=2)

plt.plot(m.time,q.value,'k.-',lw=2)

Changed line 15 from:

'''Objective:''' Design a model predictive controller for an overhead crane. Meet specific control objectives by tuning the controller and using the state space model of the crane system. Simulate and optimize the pendulum system with an adjustable overhead cart. ''Estimated time: 2 hours.''

to:

'''Objective:''' Design a model predictive controller for an overhead crane with a pendulum mass. Meet specific control objectives by tuning the controller and using the state space model of the crane system. Simulate and optimize the pendulum system with an adjustable overhead cart. ''Estimated time: 2 hours.''

Added lines 282-284:

%width=550px%Attach:pendulum_state_space.png

Added lines 277-326:

(:divend:)

(:toggle hide gekko_ss button show="Show GEKKO (Python) State Space Model and Controller":)

(:div id=gekko_ss:)

(:source lang=python:)

import numpy as np

from gekko import GEKKO

import matplotlib.pyplot as plt

mass = [2,5,10]

nm = len(mass)

plt.figure()

for j in range(len(mass)):

m1 = 10

m2 = mass[j]

eps = m2/(m1+m2)

A = np.array([[0,1,0,0],

[0,0,eps,0],

[0,0,0,1],

[0,0,-1,0]])

B = np.array([[0],

[1],

[0],

[-1]])

C = np.identity(4)

m = GEKKO()

x,y,u = m.state_space(A,B,C,D=None)

m.time = np.linspace(0,8,101)

fn = [0 if m.time[i]<6.2 else 1 for i in range(101)]

final = m.Param(value=fn)

u[0].status = 1

m.Obj(final * ((y[0]-1)**2 + y[1]**2 + y[2]**2 + y[3]**2))

m.options.IMODE = 6

m.solve()

plt.subplot(len(mass),1,j+1)

plt.ylabel(f'Ball={mass[j]} kg')

plt.plot(m.time,y[0], label = r"Position ($y$)")

plt.plot(m.time,y[1], label = r"Velocity ($v=\dot y$)")

plt.plot(m.time,y[2], label = r"Angle ($\theta$)")

plt.plot(m.time,y[3], label = r"Ang Vel ($q=\dot \theta$)")

plt.plot(m.time,u[0], label = r"Force Input ($u$)")

plt.legend(loc=1)

plt.show()

(:sourceend:)

Thanks to [[https://www.linkedin.com/in/derek-prestwich-b19485170/|Derek Prestwich]] for the state space implementation.

Changed line 19 from:

to:

{$\begin{bmatrix} \dot y \\ \dot v \\ \dot \theta \\ \dot q \end{bmatrix}=\begin{bmatrix} 0 & 1 & 0 & 0\\ 0 & 0 & \epsilon & 0\\ 0 & 0 & 0 & 1\\ 0 & 0 & -1 & 0 \end{bmatrix} \begin{bmatrix} y \\ v \\ \theta \\ q \end{bmatrix} + \begin{bmatrix} 0 \\ 1 \\ 0 \\ -1 \end{bmatrix} u$}

Added lines 28-31:

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/clMBeJ3FI_g" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

(:htmlend:)

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where ''m'_1_'=10'' is the mass of the cart, ''m'_2_'=1'' is the mass of the item carried, ''~~epsilon~~''~~ is ~~'~~'m'~~_~~2~~_'~~/(~~m'_~~1~~_'~~+m~~'~~_2_~~'~~)''~~, ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, ~~''theta''~~ is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

to:

where ''m'_1_'=10'' is the mass of the cart, ''m'_2_'=1'' is the mass of the item carried, {`\epsilon`} is ''m'_2_'/(m'_1_'+m'_2_')'', ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, {`\theta`} is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

Changed lines 227-228 from:

ax = fig.add_subplot(111,autoscale_on=False,xlim=(-1.5,0.5),ylim=(-1.2,0.4))

to:

ax = fig.add_subplot(111,autoscale_on=False,\

xlim=(-1.5,0.5),ylim=(-1.2,0.4))

xlim=(-1.5,0.5),ylim=(-1.2,0.4))

Changed lines 235-237 from:

mass1, = ax.plot([],[],linestyle='None',marker='s',~~markersize=40,markeredgecolor='k',color='orange',markeredgewidth=2)~~

mass2, = ax.plot([],~~[],linestyle~~='~~None~~',~~marker~~='~~o~~',~~markersize~~=~~20~~,~~markeredgecolor~~=~~'k'~~,~~color~~='~~orange~~',~~markeredgewidth~~=~~2)~~

line, ~~= ax.plot([]~~,~~[],~~'~~o-~~',color='orange',~~lw~~=~~4~~,~~markersize~~=~~6~~,~~markeredgecolor=~~'~~k~~',markerfacecolor='k')

mass2, = ax.plot([]

line

to:

mass1, = ax.plot([],[],linestyle='None',marker='s',\

markersize=40,markeredgecolor='k',\

color='orange',markeredgewidth=2)

mass2, = ax.plot([],[],linestyle='None',marker='o',\

markersize=20,markeredgecolor='k',\

color='orange',markeredgewidth=2)

line, = ax.plot([],[],'o-',color='orange',lw=4,\

markersize=6,markeredgecolor='k',\

markerfacecolor='k')

markersize=40,markeredgecolor='k',\

color='orange',markeredgewidth=2)

mass2, = ax.plot([],[],linestyle='None',marker='o',\

markersize=20,markeredgecolor='k',\

color='orange',markeredgewidth=2)

line, = ax.plot([],[],'o-',color='orange',lw=4,\

markersize=6,markeredgecolor='k',\

markerfacecolor='k')

Changed lines 263-265 from:

ani_a = animation.FuncAnimation(fig, animate, np.arange(1,len(m.time)),

~~ ~~ interval=40,blit=False,init_func=init)

to:

ani_a = animation.FuncAnimation(fig, animate, \

np.arange(1,len(m.time)), \

interval=40,blit=False,init_func=init)

np.arange(1,len(m.time)), \

interval=40,blit=False,init_func=init)

Changed line 272 from:

Thanks to [[~~Everton Colling|~~https://www.linkedin.com/in/everton-colling-0161b047/]] for the animation code in Python.

to:

Thanks to [[https://www.linkedin.com/in/everton-colling-0161b047/|Everton Colling]] for the animation code in Python.

Deleted lines 36-41:

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<iframe width="560" height="315" src="https://www.youtube.com/embed/iTaz8ro-4D4" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

Added lines 268-272:

Attach:download.png [[Attach:crane_control.zip|Crane Pendulum Solution Files in MATLAB and Python]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/iTaz8ro-4D4" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/iTaz8ro-4D4" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

Added lines 121-273:

(:toggle hide gekko_animate button show="Show GEKKO (Python) and Animation Code":)

(:div id=gekko_animate:)

(:source lang=python:)

# Contributed by Everton Colling

import matplotlib.animation as animation

import numpy as np

from gekko import GEKKO

# requires ffmpeg to save mp4 file

# available from https://ffmpeg.zeranoe.com/builds/

# add ffmpeg.exe to path such as C:\ffmpeg\bin\ in

# environment variables

#Defining a model

m = GEKKO()

#################################

#Weight of item

m2 = 5

#################################

#Defining the time, we will go beyond the 6.2s

#to check if the objective was achieved

m.time = np.linspace(0,8,100)

#Parameters

m1a = m.Param(value=10)

m2a = m.Param(value=m2)

final = np.zeros(len(m.time))

for i in range(len(m.time)):

if m.time[i] < 6.2:

final[i] = 0

else:

final[i] = 1

final = m.Param(value=final)

#MV

ua = m.Var(value=0)

#State Variables

theta_a = m.Var(value=0)

qa = m.Var(value=0)

ya = m.Var(value=-1)

va = m.Var(value=0)

#Intermediates

epsilon = m.Intermediate(m2a/(m1a+m2a))

#Defining the State Space Model

m.Equation(ya.dt() == va)

m.Equation(va.dt() == epsilon*theta_a + ua)

m.Equation(theta_a.dt() == qa)

m.Equation(qa.dt() == -theta_a -ua)

#Definine the Objectives

#Make all the state variables be zero at time >= 6.2

m.Obj(final*ya**2)

m.Obj(final*va**2)

m.Obj(final*theta_a**2)

m.Obj(final*qa**2)

#Try to minimize change of MV over all horizon

m.Obj(0.001*ua**2)

m.options.IMODE = 6 #MPC

m.solve() #(disp=False)

#Plotting the results

import matplotlib.pyplot as plt

plt.figure(figsize=(12,10))

plt.subplot(221)

plt.plot(m.time,ua.value,'m',lw=2)

plt.legend([r'$u$'],loc=1)

plt.ylabel('Force')

plt.xlabel('Time')

plt.xlim(m.time[0],m.time[-1])

plt.subplot(222)

plt.plot(m.time,va.value,'g',lw=2)

plt.legend([r'$v$'],loc=1)

plt.ylabel('Velocity')

plt.xlabel('Time')

plt.xlim(m.time[0],m.time[-1])

plt.subplot(223)

plt.plot(m.time,ya.value,'r',lw=2)

plt.legend([r'$y$'],loc=1)

plt.ylabel('Position')

plt.xlabel('Time')

plt.xlim(m.time[0],m.time[-1])

plt.subplot(224)

plt.plot(m.time,theta_a.value,'y',lw=2)

plt.plot(m.time,qa.value,'c',lw=2)

plt.legend([r'$\theta$',r'$q$'],loc=1)

plt.ylabel('Angle')

plt.xlabel('Time')

plt.xlim(m.time[0],m.time[-1])

plt.rcParams['animation.html'] = 'html5'

x1 = ya.value

y1 = np.zeros(len(m.time))

#suppose that l = 1

x2 = 1*np.sin(theta_a.value)+x1

x2b = 1.05*np.sin(theta_a.value)+x1

y2 = -1*np.cos(theta_a.value)+y1

y2b = -1.05*np.cos(theta_a.value)+y1

fig = plt.figure(figsize=(8,6.4))

ax = fig.add_subplot(111,autoscale_on=False,xlim=(-1.5,0.5),ylim=(-1.2,0.4))

ax.set_xlabel('position')

ax.get_yaxis().set_visible(False)

crane_rail, = ax.plot([-1.5,0.5],[0.2,0.2],'k-',lw=4)

start, = ax.plot([-1,-1],[-1.5,1],'k:',lw=2)

objective, = ax.plot([0,0],[-1.5,1],'k:',lw=2)

mass1, = ax.plot([],[],linestyle='None',marker='s',markersize=40,markeredgecolor='k',color='orange',markeredgewidth=2)

mass2, = ax.plot([],[],linestyle='None',marker='o',markersize=20,markeredgecolor='k',color='orange',markeredgewidth=2)

line, = ax.plot([],[],'o-',color='orange',lw=4,markersize=6,markeredgecolor='k',markerfacecolor='k')

time_template = 'time = %.1fs'

time_text = ax.text(0.05,0.9,'',transform=ax.transAxes)

start_text = ax.text(-1.06,-1.1,'start',ha='right')

end_text = ax.text(0.06,-1.1,'objective',ha='left')

def init():

mass1.set_data([],[])

mass2.set_data([],[])

line.set_data([],[])

time_text.set_text('')

return line, mass1, mass2, time_text

def animate(i):

mass1.set_data([x1[i]],[y1[i]+0.1])

mass2.set_data([x2b[i]],[y2b[i]])

line.set_data([x1[i],x2[i]],[y1[i],y2[i]])

time_text.set_text(time_template % m.time[i])

return line, mass1, mass2, time_text

ani_a = animation.FuncAnimation(fig, animate, np.arange(1,len(m.time)),

interval=40,blit=False,init_func=init)

ani_a.save('Pendulum_Control.mp4',fps=30)

plt.show()

(:sourceend:)

Thanks to [[Everton Colling|https://www.linkedin.com/in/everton-colling-0161b047/]] for the animation code in Python.

(:divend:)

Changed lines 31-32 from:

Attach:crane_~~pendulum1~~.~~gif~~

to:

Attach:crane_pendulum.png

Changed line 35 from:

Attach:crane_pendulum5.~~gif~~

to:

Attach:crane_pendulum5.png

Changed lines 31-32 from:

Attach:crane_~~pendulum~~.~~png~~

to:

Attach:crane_pendulum1.gif

Changed line 35 from:

Attach:crane_pendulum5.~~png~~

to:

Attach:crane_pendulum5.gif

Changed line 33 from:

'''Solution with ''m'_2_'''=~~1~~'''

to:

'''Solution with ''m'_2_'''=5'''

Added lines 29-30:

'''Solution with ''m'_2_'''=1'''

Added lines 32-35:

'''Solution with ''m'_2_'''=1'''

Attach:crane_pendulum5.png

Changed line 15 from:

'''Objective:''' Design a model predictive controller ~~with a custom objective function that satisfies a specific problem criterion. Simulate~~ and ~~optimize a pendulum system with an adjustable overhead cart~~. ~~''Estimated time: 3~~ hours.''

to:

'''Objective:''' Design a model predictive controller for an overhead crane. Meet specific control objectives by tuning the controller and using the state space model of the crane system. Simulate and optimize the pendulum system with an adjustable overhead cart. ''Estimated time: 2 hours.''

Changed lines 92-107 from:

plt.subplot(~~5~~,1,1)

plt.plot(m.time,u.value~~)~~

plt.ylabel('~~u~~')

plt.~~subplot~~(~~5,1,2~~)

plt.~~plot~~(~~m.time~~,~~v.value~~)

plt.~~ylabel~~(~~'velocity'~~)

plt.~~subplot~~(~~5~~,~~1~~,~~3~~)

plt.~~plot~~(~~m.time~~,~~y.value~~)

plt.ylabel('~~y~~')

plt.subplot(~~5~~,1,~~4~~)

plt.plot(m.time,~~theta~~.value~~)~~

plt.ylabel('~~theta~~')

plt.~~subplot~~(~~5~~,~~1,5~~)

plt.~~plot~~(~~m~~.~~time~~,~~q.value~~)

plt.~~ylabel~~(~~'q')~~

plt.~~xlabel('~~time')

plt.plot(m.time,u.value

plt.ylabel(

plt.

plt.

plt.

plt.

plt.

plt.ylabel('

plt.subplot(

plt.plot(m.time,

plt.ylabel(

plt.

plt.

plt.

plt

to:

plt.subplot(4,1,1)

plt.plot(m.time,u.value,'r-',LineWidth=2)

plt.ylabel('Force')

plt.legend(['u'],loc='best')

plt.subplot(4,1,2)

plt.plot(m.time,v.value,'b--',LineWidth=2)

plt.legend(['v'],loc='best')

plt.ylabel('Velocity')

plt.subplot(4,1,3)

plt.plot(m.time,y.value,'g:',LineWidth=2)

plt.legend(['y'],loc='best')

plt.ylabel('Position')

plt.subplot(4,1,4)

plt.plot(m.time,theta.value,'m-',LineWidth=2)

plt.plot(m.time,q.value,'k.-',LineWidth=2)

plt.legend([r'$\theta$','q'],loc='best')

plt.ylabel('Angle')

plt.xlabel('Time')

plt.plot(m.time,u.value,'r-',LineWidth=2)

plt.ylabel('Force')

plt.legend(['u'],loc='best')

plt.subplot(4,1,2)

plt.plot(m.time,v.value,'b--',LineWidth=2)

plt.legend(['v'],loc='best')

plt.ylabel('Velocity')

plt.subplot(4,1,3)

plt.plot(m.time,y.value,'g:',LineWidth=2)

plt.legend(['y'],loc='best')

plt.ylabel('Position')

plt.subplot(4,1,4)

plt.plot(m.time,theta.value,'m-',LineWidth=2)

plt.plot(m.time,q.value,'k.-',LineWidth=2)

plt.legend([r'$\theta$','q'],loc='best')

plt.ylabel('Angle')

plt.xlabel('Time')

Added lines 34-110:

(:toggle hide gekko button show="Show GEKKO (Python) Code":)

(:div id=gekko:)

(:source lang=python:)

#%%Import packages

import numpy as np

from gekko import GEKKO

import matplotlib.pyplot as plt

#%% Build model

#initialize GEKKO model

m = GEKKO()

#time

m.time = np.linspace(0,7,71)

#Parameters

mass1 = m.Param(value=10)

mass2 = m.Param(value=1)

final = np.zeros(np.size(m.time))

for i in range(np.size(m.time)):

if m.time[i] >= 6.2:

final[i] = 1

else:

final[i] = 0

final = m.Param(value=final)

#Manipulated variable

u = m.Var(value=0)

#Variables

theta = m.Var(value=0)

q = m.Var(value=0)

#Controlled Variable

y = m.Var(value=-1)

v = m.Var(value=0)

#Equations

m.Equations([y.dt() == v,

v.dt() == mass2/(mass1+mass2) * theta + u,

theta.dt() == q,

q.dt() == -theta - u])

#Objective

m.Obj(final * (y**2 + v**2 + theta**2 + q**2))

m.Obj(0.001 * u**2)

#%% Tuning

#global

m.options.IMODE = 6 #control

#%% Solve

m.solve()

#%% Plot solution

plt.figure()

plt.subplot(5,1,1)

plt.plot(m.time,u.value)

plt.ylabel('u')

plt.subplot(5,1,2)

plt.plot(m.time,v.value)

plt.ylabel('velocity')

plt.subplot(5,1,3)

plt.plot(m.time,y.value)

plt.ylabel('y')

plt.subplot(5,1,4)

plt.plot(m.time,theta.value)

plt.ylabel('theta')

plt.subplot(5,1,5)

plt.plot(m.time,q.value)

plt.ylabel('q')

plt.xlabel('time')

plt.show()

(:sourceend:)

(:divend:)

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(:title ~~Control Basics: PID, LQR, MPC~~:)

to:

(:title Model Predictive Control:)

Deleted lines 20-23:

m2 = 1

Changed line 25 from:

The objective of the controller is to adjust the force on the cart to move the pendulum mass to a new final position. Ensure that initial and final velocities and angles of the pendulum are zero. The position of the pendulum mass is initially at -1 and it is desired to move it to the new position of 0 within 6.2 seconds. Demonstrate controller performance with changes in the pendulum position and that the final pendulum mass remains at the final position without oscillation.

to:

The objective of the controller is to adjust the force on the cart to move the pendulum mass to a new final position. Ensure that initial and final velocities and angles of the pendulum are zero. The position of the pendulum mass is initially at -1 and it is desired to move it to the new position of 0 within 6.2 seconds. Demonstrate controller performance with changes in the pendulum position and that the final pendulum mass remains at the final position without oscillation. How does the solution change if the mass of the item carried is increased to ''m'_2_'=5''?

Changed lines 21-25 from:

to:

m1 = 10

m2 = 1

where ''m'_1_'=10'' is the mass of the cart, ''m'_2_'=1'' is the mass of the item carried, ''epsilon'' is ''m'_2_'/(m'_1_'+m'_2_')'', ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, ''theta'' is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

m2 = 1

where ''m'_1_'=10'' is the mass of the cart, ''m'_2_'=1'' is the mass of the item carried, ''epsilon'' is ''m'_2_'/(m'_1_'+m'_2_')'', ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, ''theta'' is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

Added lines 30-33:

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<iframe width="560" height="315" src="https://www.youtube.com/embed/iTaz8ro-4D4" frameborder="0" allowfullscreen></iframe>

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Changed lines 5-11 from:

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems with linear models. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models]].

to:

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems with linear models.

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/YHAA-uXhI0E?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models]].

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/YHAA-uXhI0E?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models]].

Changed lines 5-6 from:

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems~~. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models and large-scale constrained optimizers~~]].

to:

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems with linear models. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models]].

Changed lines 9-18 from:

'''Objective:''' Design a ~~controller to maintain temperature of ~~a ~~chemical reactor. Develop 3 separate controllers (PID, Linear MPC, Nonlinear MPC) in Python or MATLAB/Simulink. Demonstrate controller performance with steps in the set point and disturbance changes~~.~~ ~~''~~Estimated time: 3 hours.''~~

[[Attach:cstr_control.pdf|Problem Statement]]

Attach:download.png [[Attach:~~cstr~~_~~control~~.~~zip|CSTR Source Files]]~~

Attach:cstr.png

A reactor is used to convert a hazardous chemical'''~~A~~'''~~ to an acceptable chemical ~~'''~~B~~'''~~ in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes~~ the ~~conversion~~ of ~~reactant ~~''~~'A''' to product '''B''' with an irreversible and exothermic reaction. It is desired~~ to ~~maintain~~ the ~~temperature at~~ a ~~constant setpoint that maximizes the destruction of A (highest possible temperature)~~.

[[Attach:cstr_control.pdf|Problem Statement]]

Attach:download.png [[Attach

Attach:cstr.png

A reactor is used to convert a hazardous chemical

to:

'''Objective:''' Design a model predictive controller with a custom objective function that satisfies a specific problem criterion. Simulate and optimize a pendulum system with an adjustable overhead cart. ''Estimated time: 3 hours.''

A pendulum is described by the following dynamic equations:

Attach:pendulum_crane_equations.png

where ''epsilon'' is ''m'_2_'/(m'_1_'+m'_2_')'', ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, ''theta'' is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

Attach:pendulum_crane.png

The objective of the controller is to adjust the force on the cart to move the pendulum mass to a new final position. Ensure that initial and final velocities and angles of the pendulum are zero. The position of the pendulum mass is initially at -1 and it is desired to move it to the new position of 0 within 6.2 seconds. Demonstrate controller performance with changes in the pendulum position and that the final pendulum mass remains at the final position without oscillation.

A pendulum is described by the following dynamic equations:

Attach:pendulum_crane_equations.png

where ''epsilon'' is ''m'_2_'/(m'_1_'+m'_2_')'', ''y'' is the position of the overhead cart, ''v'' is the velocity of the overhead cart, ''theta'' is the angle of the pendulum relative to the cart, and ''q'' is the rate of angle change'^2^'.

Attach:pendulum_crane.png

The objective of the controller is to adjust the force on the cart to move the pendulum mass to a new final position. Ensure that initial and final velocities and angles of the pendulum are zero. The position of the pendulum mass is initially at -1 and it is desired to move it to the new position of 0 within 6.2 seconds. Demonstrate controller performance with changes in the pendulum position and that the final pendulum mass remains at the final position without oscillation.

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Attach:download.png [[Attach:crane_control.zip|Crane Pendulum Solution Files in MATLAB and Python]]

# Bryson, A.E., Dynamic Optimization, Addison-Wesley, 1999.

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'''Objective:''' Design a ~~model predictive controller to maintain temperature of a chemical ~~reactor. Develop ~~a linear, first-order model of the reactor and implement the controller~~ in Python or MATLAB/Simulink. Demonstrate controller performance with steps in the set point and disturbance changes. ''Estimated time: 3 hours.''

to:

'''Objective:''' Design a controller to maintain temperature of a chemical reactor. Develop 3 separate controllers (PID, Linear MPC, Nonlinear MPC) in Python or MATLAB/Simulink. Demonstrate controller performance with steps in the set point and disturbance changes. ''Estimated time: 3 hours.''

Changed lines 9-13 from:

'''Objective:''' Design a model predictive controller to maintain temperature of a chemical reactor. Develop a linear, first-order model of the reactor and implement the ~~model~~ in Python or MATLAB/Simulink. ~~''Estimated time: 3 hours~~.''

to:

'''Objective:''' Design a model predictive controller to maintain temperature of a chemical reactor. Develop a linear, first-order model of the reactor and implement the controller in Python or MATLAB/Simulink. Demonstrate controller performance with steps in the set point and disturbance changes. ''Estimated time: 3 hours.''

[[Attach:cstr_control.pdf|Problem Statement]]

Attach:download.png [[Attach:cstr_control.zip|CSTR Source Files]]

[[Attach:cstr_control.pdf|Problem Statement]]

Attach:download.png [[Attach:cstr_control.zip|CSTR Source Files]]

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There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models and large-scale constrained optimizers]].

to:

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models and large-scale constrained optimizers]].

!!!! Exercise

'''Objective:''' Design a model predictive controller to maintain temperature of a chemical reactor. Develop a linear, first-order model of the reactor and implement the model in Python or MATLAB/Simulink. ''Estimated time: 3 hours.''

Attach:cstr.png

A reactor is used to convert a hazardous chemical '''A''' to an acceptable chemical '''B''' in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes the conversion of reactant '''A''' to product '''B''' with an irreversible and exothermic reaction. It is desired to maintain the temperature at a constant setpoint that maximizes the destruction of A (highest possible temperature).

!!!! Solution

!!!! Exercise

'''Objective:''' Design a model predictive controller to maintain temperature of a chemical reactor. Develop a linear, first-order model of the reactor and implement the model in Python or MATLAB/Simulink. ''Estimated time: 3 hours.''

Attach:cstr.png

A reactor is used to convert a hazardous chemical '''A''' to an acceptable chemical '''B''' in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes the conversion of reactant '''A''' to product '''B''' with an irreversible and exothermic reaction. It is desired to maintain the temperature at a constant setpoint that maximizes the destruction of A (highest possible temperature).

!!!! Solution

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(:title Control Basics: PID, LQR, MPC:)

(:keywords Proportional, Integral, Derivative, Linear Quadratic Regulator, Model Predictive Control, simulation, modeling language, differential, algebraic, tutorial:)

(:description There are many methods to implement control including basic strategies such as PID or more advanced such as Model Predictive techniques:)

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models and large-scale constrained optimizers]].

(:keywords Proportional, Integral, Derivative, Linear Quadratic Regulator, Model Predictive Control, simulation, modeling language, differential, algebraic, tutorial:)

(:description There are many methods to implement control including basic strategies such as PID or more advanced such as Model Predictive techniques:)

There are many methods to implement control including basic strategies such as a proportional-integral-derivative (PID) controller or more advanced methods such as model predictive techniques. The purpose of this section is to provide a tutorial overview of potential strategies for control of nonlinear systems. A following section relates methods to [[Main/NonlinearControl | implement dynamic control with nonlinear models and large-scale constrained optimizers]].