Whether it's single microcontrollers handling motor control
or an automobile assembly line containing multiple robots,
you can be sure mechatronics is in the mix. Simulating such
complex systems allows developers to build without having
the hardware in hand. This is critical when some or all of the
hardware doesn’t exist, but becomes even more valuable when
considering “what if ” scenarios.
However, two major issues continue to crop up: speed and
complexity. Larger systems, more detailed simulation, and a
host of other factors push the need for high-performance host
development systems. Tradeoffs between simulation speed and
the level of accuracy must often be made because of available
resources. Faster processors always help, but the trend toward
multicore systems actually works in favor of simulation because
the systems being simulated are distributed as well.
The environment is more complex than typical programming
environments because of physical concerns. For example,
Figure 1 shows a control system for a Stewart platform commonly
used in production lines and many other industrial applications. The graphical programming environment is the
MathWorks’ Simulink.
The SimMechanics add-on to Simulink highlights the kind
of simulation environment needed for today’s mechatronics
development requirements. SimMechanics complements the
MathWorks’ other simulation tools, including SimElectronics,
SimDriveline, SimHydraulics, and SimPowerSystems.
In this case, there are effectively two models: the simulated
physical world model and the application model. The latter
occurs because Simulink and Matlab are model-based development
tools, so the application is a model. The physical model
accounts for the physics-based simulated environment. The
application model interacts with this environment to simulate
the application running in the real world.
The mechanical aspects of the physical world represent just
the start in many designs, due to the growing importance of other
considerations. For instance, temperature, hydraulics power,
and radio transmission often come into play with applications
that range from robotics to cell-phone design. Often, this is
where the CAD realm merges with the programming realm.
Several CAD vendors, such as Autodesk and Solidworks,
have advanced packages designed to handle simulations,
though they’re typically oriented toward physical construction
versus process-control development. SolidWorks Simulation
Premium contains advanced finite-element-analysis (FEA)
support. The package can target stress under dynamic load.
It also addresses nonlinear analysis like deflection and impact
with flexible materials such as foam, rubber, and plastic.
CAD designers are familiar with modular construction.
Remmele Engineering uses models of Nook Industries’ actuators
and components to create assembly-line designs using
SolidWorks (Fig. 2). Also, Remmele Engineering develops
products for a range of applications from novel drug-delivery
systems to energy-storage systems that require physical as well
as control components.
Nook Industries offers a line of linear actuators that typically
wind up in computer-controlled systems. The company’s
Web site is set up to deliver 2D/3D models of its products so
a designer can drop in an array of linear actuators and develop
a virtual device or production line. This approach is common
for companies that deliver physical devices, but less so when it
comes to support for control applications.
Simulation and analysis of physical entities is useful in a
design that doesn’t include a computer-based controller. However,
if there is one, it can be even more valuable due to this type of system’s greater complexity. Most CAD packages
work with software-development tools, too.
Take Solidworks and National Instruments,
who have worked together to
integrate Solidworks’ COSMOSMotion
with National Instruments’ LabVIEW
(see “Cooperation Leads To Complex, Real-
World Simulations” at www.electronicdesign.
com, ED Online 17273). This type of
integration allows CAD designers to prepare
object models of physical entities like
gears, arms, and boxes while programmers
concentrate on the feedback and control
algorithms that will handle the motors
and actuators within the system.
Tying objects together enables the models to cooperate,
and rendering systems permit visualization of the models in
action. Either modeling environment alone can demand significant
amounts of computing power, and rendering can tax the best
graphics subsystems. Putting it all together can burden even the
most powerful systems when creating large models. At this stage,
multicore hosts can make a significant difference.
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