Simulating Simulink Models with the sim Command

Use this skill to generate simulation results for analysis. For persistent, reusable pass/fail behavioral testing (especially of individual subsystems), use

testing-simulink-models

instead.

When to Use

• Running a Simulink model from a MATLAB script
• Configuring simulation parameters (StopTime, solver, etc.) programmatically
• Passing input signals to root-level Inport blocks
• Accessing logged signal data after simulation
• Running parameter sweeps or batch simulations

When NOT to Use

• Writing declarative Gherkin-based tests → use

  testing-simulink-models

• Testing an individual subsystem or component → use

  testing-simulink-models

  (requires Simulink Test; auto-creates a harness, compiles only the subsystem — much faster than

  sim()

  which always compiles the entire model)

Minimal working pattern

Always simulate using

Simulink.SimulationInput

and

Simulink.SimulationOutput

:

in = Simulink.SimulationInput('MyModel');
in = in.setModelParameter('StopTime', '10');
out = sim(in);

Setting parameters

Use

SimulationInput

methods to configure the simulation:

% Model-level parameters (StopTime, SolverType, SimulationMode, etc.)
in = in.setModelParameter('StopTime', '10', 'SolverType', 'Fixed-step');

% Block parameters
in = in.setBlockParameter('MyModel/Gain', 'Gain', '5');

% MATLAB workspace variables used by the model
in = in.setVariable('Kp', 1.2);

Input signals

Pass input signals through Inport blocks using a

Simulink.SimulationData.Dataset

. Elements are matched to Inport blocks by index position — the first element maps to the Inport with port number 1, the second to port number 2, and so on.

dt = 0.01;
N = 1000;
t = dt*(0:N)';
u = sin(2*pi*t);

ts = timeseries(u, t);

ds = Simulink.SimulationData.Dataset;
ds{1} = ts;

in = in.setExternalInput(ds);
out = sim(in);

You can also use

timetable

as an input format:

secs = seconds(t);
tt = timetable(secs, u);

ds = Simulink.SimulationData.Dataset;
ds{1} = tt;

in = in.setExternalInput(ds);

Discovering logged data

First, discover what kinds of logged data the model produces using

who

, then inspect signal names within

logsout

:

in = Simulink.SimulationInput('MyModel');
out = sim(in);

% See what logging properties exist (logsout, yout, tout, etc.)
who(out)

% List individual signal names within logsout
disp(out.logsout.getElementNames);

Accessing logged data

Logged signals are available through

out.logsout

. Access them directly by name:

% Plot a logged signal
plot(out.logsout.get('signalName').Values)

% Get time and data separately
sig = out.logsout.get('signalName').Values;
plot(sig.Time, sig.Data)

Multiple simulations

When running many simulations, create an array of

Simulink.SimulationInput

objects:

in = repmat(Simulink.SimulationInput('MyModel'),N,1);
for k = 1:N
    in(k) = Simulink.SimulationInput('MyModel');
    in(k) = in(k).setVariable('gain', gains(k));
end
out = sim(in);

To enable fast restart for iterative sweeps (compiles the model only once):

out = sim(in, 'UseFastRestart', 'on');

Parallel simulation (parsim)

To run multiple simulations in parallel, use

parsim

instead of looping over

sim

:

for k = 1:N
    in(k) = Simulink.SimulationInput('MyModel');
    in(k) = in(k).setVariable('gain', gains(k));
end
out = parsim(in);

parsim

also supports

'UseFastRestart','on'

for faster batch runs.

Guardrails

• Never use

  set_param

  ,

  load_system

  , or

  open_system

  to drive simulation —

  SimulationInput

  replaces all of these.
• Never wrap

  SimulationOutput

  access in

  try-catch

  or

  isfield

  —

  sim

  either returns a valid object or throws.

  SimulationOutput

  has no

  isfield

  method.
• Never create unnecessary intermediate variables for logged data — access directly via

  out.logsout.get('name').Values

  .
• Always use

  in

  /

  out

  as variable names for

  SimulationInput

  /

  SimulationOutput

  .
• Always use

  setExternalInput

  with a

  Dataset

  — don't pass comma-separated lists of variables.