This document describes an experiment using LabVIEW to design and implement a robust DC motor controller. It discusses: 1. The apparatus used, including the LabVIEW software and computer specifications. 2. An overview of LabVIEW, describing it as a visual programming language where programs are created by connecting functional nodes with wires to control data flow. 3. The procedure for the experiment, which involves modeling the DC motor and controller, discretizing the controller, and performing time and frequency response simulations to analyze robustness.

1. EXPERIMENT 8
AIM:
To study and design the implementation of CDEx Robust DC Motor.
Apparatus Used:s
Microsoft Windows XP Professional Version 2002, Intel(R) Pentium(R) Dual CPU, E2180
@2.00 GHz, 2.00 GHz, 1.99 GB of RAM, LabVIEW Robotics 2011 SPI.
Theory:
LabVIEW (short for Laboratory Virtual Instrumentation Engineering Workbench) is a
platform and development environment for a visual programming language from National
Instruments. Short for Laboratory Virtual Instrument Engineering Work bench is a programming
environment in which you create programs using a graphical notation (connecting functional nodes
via wires through which data flows); in this regard, it differs from traditional programming
languages like C, C++, or Java, in which you program with text. However, LabVIEW is much
more than a programming language. It is an interactive program development and execution
system designed for people, like scientists and engineers, who need to program as part of their jobs.
The LabVIEW development environment works on computers running Windows, Mac OS X, or
Linux. LabVIEW can create programs that run on those platforms, as well as Microsoft Pocket PC,
Microsoft Windows CE, Palm OS, and a variety of embedded platforms, including Field
Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs), and microprocessors.
Procedure:
Execution is determined by the structure of a graphical block diagram on which the
programmer connects different function nodes by drawing wires. These wires propagate variables
and any node can execute as soon as all its input data become available. LabVIEW ties the
creation of user interfaces (called front panels) into the development cycle. LabVIEW
programs/subroutines are called virtual instruments (VIs). Each VI has three components: a block
diagram, a front panel, and a connector panel. The last is used to represent the VI in the block
diagrams of other, calling VI. Controls and indicators on the front panel allow an operator to
input data into or extract data from a running virtual instrument. However, the front panel can
also serve as a programmatic interface. Thus a virtual instrument can either be run as a program,
with the front panel serving as a user interface, or when dropped as a node onto the block
diagram, the front panel defines the inputs and outputs for the given node through the connector
pane. This implies each VI can be easily tested before being embedded as a subroutine into a larger
program. The graphical approach also allows non-programmers to build programs simply by
dragging and dropping virtual representations of lab equipment with which they are
already familiar.

2. Execution of VI’s and Sub-VI’s:
Main VIs:
This illustrates the use of classical control design for a DC motor plant, allowing the user to study
different topologies of PID controllers, choose the correct Sampling Time for the discrete controller
and analyze the Time and Frequency response of the system. In the end, we test the robustness of the
Controller, simulating several models based on the uncertainty of the parameters.
1. Modeling:
2. Modeling with Uncertainty:

3. 3. PID Controller:

4. 4. Discretization:

5. 5. Frequency Response:
6. Time Domain Linear Simulation:

6. 7. Robust Analysis:

7. Block Diagram:

8. Result:
One of the most important issues in control system design is to ensure the stability of the plant. PID
controller used in industrial solutions still represents the most common controller in industry.
However PID can only guess stability area and indicates stability zone by trial and error together
with the experience of the designer. Decrement of system performance index leads to easier and
better control system stability. Integral time absolute error (ITAE) is one of the most criterion used to
reduce system error and give the best PID gain values for a desired system response requirements.
In modern control theory its assume that required system performance can easily be specified
properly, where the performance index is calculated and measured or used to obtain the whole
system behaviour quantitatively [1]. We consider control system with feedback shown in Fig (1)
bellow where the close loop transfer function is:
The selection of PID controllers is basically a search problem in a three dimensional space, and by
choosing different points of parameter space we can produce different step response for a step input.
PID controller can be determined by moving this search point by trial and error basis. The main
problem in the selection of PID coefficients is that they do not meet the desired performance index or
robust control system that the designer requires, in this section we will produce one of several robust
PID controller design method which are:
Consider a DC motor with a transfer function as in equation (8) and block diagram as shown in
figure bellow, its assume that the input of the system is the voltage source (V) applied to the motor's
armature, where the output is the rotational speed of the shaft 𝑑𝜃/𝑑𝑡. The rotor and shaft are
assumed to be static. We also assume a viscous friction model, that is, the friction torque is
proportional to shaft angular velocity as in the following.
Fig. DC motor speed control
The design and implementation of a PC-based dc motor velocity system using both special optimal
control and PID have been presented in this paper, the simulation results reveal that using second
controller to the velocity control application, has shorter settling time, and smaller over shoot

9. amplitude. Comparisons of simulation results show that the integral state feedback controller
combined by kalman filter due to better robustness performance than the PID controller. We find the
Cost Function optimal weighting matrices Q and R by Genetic Algorithm. Simulation results show
that by this strategy we have optimal system characteristics (settling time, overshoot amplitude).
Precaution:
 To avoid hanging the user interface with front panel locking, configure all events you want a VI
to handle in a single Event structure or always make sure there is only one Event structure in a
loop.
 Additionally, make sure there is always an Event structure available to handle events as they
occur.