DYNAMIC-SYSTEM MODELS AND COMPUTER PROGRAMS

Computer Modeling and Simulation
Simulation is experimentation with models. Simulation for engineering
design, research, and education studies must rapidly exercise a wide variety
of models and then store and access a large volume of results. This is practi-
cal only with models programmed on computers.
Dynamic-system models relate model-system states to earlier states. Classical
physics, for example, predicts continuous changes of quantities such as position,
velocity, or voltage with continuous time. Computer simulation of such systems
started in the aerospace industry and is now indispensable in biology, medicine,
and agroecology as well as in all engineering disciplines. At the same time, dis-
crete-event simulation has gained importance for business and military planning.
Simulation is at its best when combined with mathematical analyses. But
simulation results can often provide insight and possibly useful decisions
where exact analysis is impractical. This was true for many early control-
system optimizations. As another example, Monte Carlo simulations simple-
mindedly measure statistics over repeated experiments to solve problems that
are too complicated for probability theory analysis. Simulation results must
eventually be validated by real experiments, just like analytical results.


Computer simulations can be speeded up or slowed down at the experi-
menter’s convenience. You can simulate a flight to Mars or to Alpha Centauri
in one second. Periodic clock interrupts synchronizing suitably scaled simu-
lations with real time permit “hardware in the loop”: you can “fly” a real
autopilot—or a real human pilot—on a tilting platform controlled by a com-
puter flight simulation. In this book, we are interested in very fast simulation,
for we want to study the effects of many different model changes.
Specifically, we want to
1. Enter and edit programs in convenient editor windows.
2. Use typed or graphical-interface commands to start, stop, and pause
simulations, to select displays, and to make parameter changes. Results
ought to appear immediately to provide an intuitive “feel” for the
effects of model changes (interactive modeling).
3. Program systematic parameter-influence studies and produce cross-
plots or tables.
4. Program model changes to optimize effectiveness measures, and study
effects of random parameter changes or random model inputs by taking
statistics on repeated simulations (Monte Carlo simulation).