Study of model predictive control using ni lab view

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSNIN –
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH 0976
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME
ENGINEERING AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print) IJARET
ISSN 0976 - 6499 (Online)
Volume 3, Issue 2, July-December (2012), pp. 257-266
© IAEME: www.iaeme.com/ijaret.asp
©IAEME
Journal Impact Factor (2012): 2.7078 (Calculated by GISI)
www.jifactor.com

STUDY OF MODEL PREDICTIVE CONTROL USING NI LabVIEW
Dr.V.BALAJI
Principal cum Professor, Department of Electrical and Electronics Engineering
Lord Ayyappa Institute of Engineering and Technology,
Kanchipuram. India
Email Id: balajieee79@gmail.com

ABSTRACT

This paper introduces the application of virtual instruments implemented using the national
instruments LabView software with various objectives in control system engineering
education. The main of this paper is to provide a better understanding in the performance of
model predictive control (MPC). This current paper discuses how to create a MPC for a
simple model, MPC simple model with time delay and MPC versus PID controller. The scope
of this paper is to give an overview of the MPC implementation in LabVIEW. The simulated
results clearly explain the performance of the MPC and the difference between MPC and PID
controller.
Keywords: Control systems, Graphical Programming, Model Predictive Control (MPC), NI
LabVIEW, PID controller, Simulation, Software
I INTRODUCTION
Now a day’s control systems engineers in the industry are using computer aided control
systems design for modeling, system identification and estimation. These make a way to
study graphical programming software tools and also becoming indispensable for teaching
control systems theory and its applications. By adopting simulations the students may easily
visualize the effect of adjusting different parameters of a system and the overall performance
of the system can be viewed. Moreover it would be a ideal if such tools are not only utilized
in relevant industries but it also be taught in the classroom.NI Labview has proven to be an
invaluable tool in decreasing development time in research, design, validation, production
and manufacturing cost. The major advantages of labview include ease of learning,
debugging, and simplicity of using interface, reliable performance and capability of
controlling the equipment.
In this paper it is demonstrated how to create a model predictive control for a first order
system, first order system with time delay in a Lab VIEW environment and also explains
virtually the difference between MPC and PID controller. The simulations are conducted
using control design simulation tool box in a graphical programming environment. Section 2
of this research paper is brief introduction of Model Predictive control. Section 3 is about the
introduction of NI Labview. Section 4 deals with implementation of MPC in Lab VIEW.
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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –

Section 5 describes the simulation results of MPC. Section 6 is the conclusion of this research
paper. Section 7 contains the reference used in this paper.

II INTRODUCTION TO MPC
Model Predictive Control, or MPC, is an advanced method of process control that has been
in use in the process industries such as chemical plants and oil refineries. Model predictive
controllers rely on dynamic models of the process, most often linear empirical models
obtained by system identification. Model predictive control (MPC) refers to a class of
computer control algorithms that utilize an explicit process model to predict the future
response of a plant. At each control interval an MPC algorithm attempts to optimize future
plant behavior by computing a sequence of future manipulated variable adjustments. The first
input in the optimal sequence is then sent into the plant, and the entire calculation is repeated
at subsequent control intervals.
Model predictive control (MPC) is a technique that focuses on constructing controllers that
can adjust the control action before a change in the output set point actually occurs. This
predictive ability, when combined with traditional feedback operation, enables a controller to
make adjustments that are smoother and closer to the optimal control action values. MPC
consists of an optimization problem at each time instants, k. The main point of this
optimization problem is to compute a new control input vector to be feed to the system, and at
the same time take process constraints into considerations. An MPC algorithm consists of a
Cost function, Constraints , Model of the process .

I II INTRODUCTION TO NI LABVIEW SOFTWARE
LabVIEW StandS for Laboratory Virtual Instrumentation Engineering Workbench. The
Labview environment consists of two programming layers a front panel and a block diagram
.The front panel is built with controls and indicators, which are the interactive input and
output terminals of the VI respectively. LabVIEW has many built in functions such as I/O
data communication, state charts, Mathematics, Signal Processing, System Identification and
Estimation. Control Design Simulation Module. Using above mentioned functions of
LabVIEW MPC Model was simulated.

IV CONTROL DESIGN AND SIMULATION USING LABVIEW
4.1Model Construction
The Control Design and Simulation and predictive control palette in LabVIEW is shown in
figure 1 and 2 respectively.

Figure 1 The Control Design Palette in LabVIEW

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Figure 2 The Predictive Control Palette in LabVIEW

The Model Construction Palette is shown in figure 3 and also shows how many types models
is available in the control design and simulation module.

Figure 3 The Model Construction Palette

The VIs in this section is used to construct various types of Models like State Space, Transfer
Function, and Zero-Pole-Gain. The Construct State Space Model and Construct Transfer
Function Model functions are shown in figure 4 and 5 respectively. We use the CD Create
MPC Controller VI to create an MPC Controller. The MPC created on a state-space model.
The CD implement MPC Controller is used to calculate the control values for each
sampling time and it is implemented in a While Loop.

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4.2 CD Construct State Space Model

Figure 4 CD Construct State Space Model.VI

The terminals for the function are shown above. If the Sampling Time terminal is not
connected, the system is by default considered continuous. Connecting a value to Sampling
Time will change the system to discrete time using the given sampling time. There are
terminals for the A, B, C, and D matrices of the State Space model. Once LabVIEW creates
the State-Space model (available at the output terminal), it can be used for other functions
and can be converted into other forms.
4.3 CD Construct Transfer Function Model

Figure 5 CD Transfer Function Model.VI
The terminals are shown above. The important terminals are the Numerator and
Denominator. As in the previous case, once the model is created, it can either be displayed on
the Front Panel or connected to other functions.
4.4 CONSTRUCTION OF PID ACADEMIC CONTROLLER
The VI shown below shows how to create and display an PID Academic controller .ie
standard parallel PID controller.

Figure 6 Block Diagram of PID Academic
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V SIMULATION OF MPC IN LabVIEW
5.1 First order Model
In this section we will consider a first model using LabVIEW Consider a first order system
given below.

Where
T is the time constant for the system
K is the pump gain
We set T = 8s and K = 4
Substitute the values in the above equation we get

The front Panel diagram with a wave form a simple model is shown in the figure 7.
.

Figure 7 Front panel Diagram for a Simple Model
From the wave form we clearly understand the Performance of MPC how it moves to reach
the set point.

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Constraints and Weighting

5.2 Model with Delay Time
We consider the following system

X = - 1/T + Ku (t- )

We set the values as T = 8s and K = 4 and =4

Where = time delay
The MPC algorithm requires that the model is a linear state-space model, but the time delay
causes problems. A solution could be to transform the differential equation we have to a
transfer function. Then we can use built-in functions in LabVIEW to convert it to a linear
state-space model. Applying LT to the above equation we get

H(s) = x(s)/u(s) =

Substitute the values as T = 8s and K = 4 and = 4 We get the final expression

H(s) = x(s)/u(s) =

The figure 8 shows the front panel diagram of a simple model with a time delay and also it
shows how MPC reaches the set point with a time delay of 4 s.

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Figure 8 Front panel Diagram of a Simple Model with a Time Delay

Figure 9 MPC Parameters

5.3 MPC VS PID Controller

Figure 9a Front Panel Diagram of MPC Controller
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Figure 10 Front Panel Diagram of PID Controller

Figure 11 Block Diagram of MPC Controller

264


Figure 12 Block Diagram of PID Controller
From the waveforms of figure 9and 10 we see the main difference between a MPC controller
and a more traditional PID controller. Another main difference between MPC and PID is that
MPC can handle MIMO (Multiple Inputs, Multiple Outputs) systems, while PID is used for
SISO systems (Single Input, Single Output). From the figure 9 & 10 we can analyze the
difference MPC and PID Controller. The difference between them is summarized below

S.No MPC Controller PID Controller

1. Constraints included in the design No knowledge about constraints

2.
A mathematical model is not needed A mathematical model is not
needed

3.
Improved process operation Not optimal process operation

4.
needed

5.
needed

Table 1 Difference between MPC and PID

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VI CONCLUSION
The idea of Model Predictive Control Simulation using NI LabVIEW is being put into
operation successfully. This paper clearly explained the depth of MPC implemented through
LabVIEW. The results illustrate the performance of MPC and also clearly state the difference
between the PID and MPC controller. These simulation results are useful to do the required
modifications in control system industry for optimal control.

VII REFERNCES
[1] Erik Luther (2012), “Introduction to Control Design and Simulation using LabVIEW”
Rice University, Houston, Texas.
[2] Camacho E. F and Bordons C. (1999) Model Predictive Control, Springer, London.
[3] Maciejowski J. M. (2002) Predictive Control with Constraint, Prentice Hall.
[4] National Instruments, 2012. LabVIEW User Manual at http://www.ni.com/pdf/manuals.
[5] http://techteach.no/labview/ by Finn Haugen.
[6] http://zone.ni.com/devzone/cda/tut/p/id/6368 based on Prof. Dawn Tilbury’stutorials from
University of Michigan.

ABOUT THE AUTHOR
Dr.V.BALAJI has 12 years of teaching experience. Now he is working
as a principal in Lord Ayyappa Institute of Engineering and Technology,
Kanchipuram. His current areas of research are model predictive control,
process control, and Fuzzy and Neural Networks. He has published 26
research papers in national and international journals and conferences. He is
a member of ISTE, IEEE , IAENG, IAOE and IACSIT.

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Study of model predictive control using ni lab view

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