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photo cells. As far as software, the
application is based on Automation
Studio - the development environment
for control and drive functions
from B&R - and the tool mAtLAB ® /
Simulink ® from the mathworks.
the mAtLAB ® Real time Workshop
embedded Coder is used to export
the drive code. the process control
itself is performed using the State-
Flow toolbox in the form of state
machines, and the Virtual Reality
toolbox is used to animate the flow
of material in 3D. The hardware is
currently limited to a B&R power
panel, i.e. a visualization with integrated
Cpu, and an ACOpOSmulti
frequency inverter for connecting
four linear drives.
Step 1: System analysis
The first step in the development
process is to analyze the system behavior.
this analysis involves testing
the actual system and the system
model. the goal here is to gather
knowledge about possible states and
behavior of the system. For the portal
arrangement, the system model
was set up as a Simulink model, with
the gripper axes represented as a
pt1 system. the gripper pneumatics
are also represented as a Simulink
model with the corresponding timing
behavior. A truth table was integrated
in the state flow diagram for
evaluation of the sensors.
Step 2: Development of the control
functions
Based on the knowledge gained during
the analysis phase, the second
step involves developing an open or
closed loop control function. In addition
to purely textual descriptions
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Image 1: Using this 3-axis portal in a
system network, researchers at the
Technical University of Munich are developing
new concepts in simulationbased
control development.
1 Y-drive
2 X-drive
3 Gripper witch pneumatic Z-drive
4 Photo cells
of the automation functions, formal
description and modeling methods
from software development may be
used, as well as task or sequence
diagrams. Once the software functions
have been designed, they are
implemented in the development environment.
State machines in state
flow provide descriptions. These are
able to represent both simple and
complex drive functions.
Connecting the developed control
function to the system model makes
it possible to perform model based
testing. In addition to verifying the
basic functions, it is also possible to
test disturbance scenarios and identify
potential adjustments to improve
performance. the design, implementation
and model-based testing
stages should be repeated until they
result in fully-functional and errorfree
control code that can be implemented
in the real system.
the input variables for simulation
module process control include the
movement variables of the X and Y
drives and the simulated sensor sig-
Image 2: Simulation
model consisting of an
open loop control model,
system and closed-loop
control models and signal
evaluation in MATLAB ® /
Simulink ® .
nals. the system states encountered
during movement are also represented
in the process controller as
states. the corresponding control
values and transition conditions are
also formulated in state flow syntax.
process movements typically require
more than binary control of
Technology
the drives. Often there are drives
with speed or position control that
are connected to one another via
electronic cam profiles for coupled
movements. the creation of these
curves is an important part of the
development of an automation
solution. Image 3 shows a classic
trapezoid movement profile with
acceleration, speed and path. the
development of the process characteristic
curves takes place entirely
in the simulation environment. this
allows complex process movements
and the corresponding control characteristic
curves to be created and
tested in the early phases of development.
Via XmL exchange format
(extended markup language) it is
then possible to implement the process
characteristic curves directly in
B&R controllers without direct access
to the target system.
Step 3: Simulation and testing
After the control functions have been
tested, they are implemented. During
the software test, the implemented
functions are linked to the simulation
model. the model then creates continuous
processes, process movements
and event-based processes in
a common system. As a result, the
overall behavior of the new system
can be imitated realistically, and errors
can be detected and corrected
early and easily. the current testing
environment is based on mAtLAB ®
and integrates the following:
■ process control model
■ System behavior model
■ process characteristic curves (control
functions)
■ Visualization of material flow
An additional 3D environment was
integrated for the gripper scenario,
which displays the movements graphically
for the developer. >>
automotion 07/2009
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