Simulink Linearization

SkillProductivity

Linearize Simulink models. Use when obtaining linear time invariant or linear parameter varying models from Simulink models.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Simulink Linearization skill

What this skill tells your AI

The instructions your AI receives, as published by matlab/simulink-agentic-toolkit in skills-catalog/control-systems/simulink-linearize/SKILL.md and read by ahel’s review.

Extract linear time invariant (LTI) or linear parameter varying (LPV) models from Simulink using linearize and related APIs from Simulink Control Design.

When to Use

  • Obtaining a linear model (tf, ss, zpk) from a Simulink model
  • Batch linearization across operating points and parameter variations
  • Building LPV models with ssInterpolant
  • Debugging linearization results (zero gain, unexpected dynamics)
  • Extracting multiple LTI systems with a single model compile

When NOT to Use

  • Frequency response estimation from simulation — use simulink-frequency-response for frestimate-based fallback
  • No Simulink model is involved

Workflow

The linearization pipeline has four stages. Not every task requires all stages.

1. Define I/O Points 2. Operating Point  →  3. Linearize  →  4. Debug
    (root level/linio)   (findop/operspec)     (linearize)     (advisor)

Stage 1: Define Linearization I/O Points

Determine I/O points using this decision sequence. Use the first case that applies:

Case A — IO points can be inferred from prompt or model context:

Use the first sub-case that matches:

  1. User specifies explicit I/O signals or blocks (e.g., "from r to y") → define linio points. All linio points must reference a block's output port. If a candidate block has no output ports (Outport, Terminator, Scope) → trace upstream to find the source block and port with model_read.

    io = [linio(sprintf("%s/InputBlock", mdl), 1, "input"); ...
          linio(sprintf("%s/OutputBlock", mdl), 1, "output")];
    
  2. User targets a specific block or subsystem (e.g., "linearize the Controller") → Use the block path as the io argument signaling linearize to perform open-loop linearization of the block

    io = sprintf("%s/Controller", mdl);
    
  3. Model has existing linearization pointsio = getlinio(mdl); — use if non-empty.

  4. Root-level Inport/Outport blocks exist → omit linio. The linearize command will linearize about the model's root-level I/Os. Use model_read at root scope (depth "0") to confirm root-level Inport/Outport blocks exist.

Case B — Cannot determine IO points:

If none of the above apply → do not guess. Ask the user which signals to use as linearization inputs and outputs. Present the available blocks/signals from the model to help them decide.

Block path rules:

  • Use sprintf for block names containing special characters (newlines, commas):
    blkPath = sprintf("%s/Integrator,\nSecond-Order", mdl);
    io = linio(blkPath, 1, "output");
    
    sub.Name = sprintf("%s/My\nBlock",mdl);
    sub.Value = replacement_lti;
    
    sys = linearize(mdl, io, sub);
    

Stage 2: Operating Point

Determine where to linearize. Choose one:

SituationApproach
Model ICsSkip — linearize uses model initial conditions
Steady-state trimoperspec → configure → findop(mdl, opSpec, findopOptions(DisplayReport="off"))
Need snapshot from simulationlinearize(mdl, tSnapshot)
Batch over parameter gridArray of operspec objects → findop(mdl, specArray, params)
Operating points knownArray of operpoint objects → configure

For batch workflows, use copy to create the operating point array:

% assign varied variable to workspace
myvar = 0;
% create base spec
opBase = operspec(mdl);
opBase.States(1).Known = true;
% define param to vary
nPts = 5;
params.Name = "myvar";
params.Value = linspace(-pi, pi, nPts);
for i = nPts:-1:1
    opArray(i) = copy(opBase);
    opArray(i).States(1).x = params.Value(i);
end
ops = findop(mdl, opArray, params, findopOptions());

Stage 3: Linearize

sys = linearize(mdl, OPTIONAL_ARGS);

Each input argument to linearize is optional (beside mdl).

sys = linearize(mdl, io, op, params, blocksub, opts);
ArgumentRequiredBehavior if ProvidedBehavior if Omitted
mdlYModel to linearizeNA
ioNlinearize at I/O pointsLinearize at root level I/Os
opNOperating points OR times to linearizeLinearize at model IC
paramsNVary parameters for each linearizationNo variation
blocksubNUser specified block linearizationsBlocks have Simulink linearization
optsNUser specified linearizeOptionsDefault options

Multi-rate models default to the LCM sample time. Use the SampleTime option to specify linear model sample time:

opts = linearizeOptions(SampleTime=0);
sys = linearize(mdl, io, opts);

Batch linearization for LPV:

opts = linearizeOptions(BatchConsistency="on", StoreOffsets="system");
sysArray = linearize(mdl, io, ops, params, opts);
lpvSys = ssInterpolant(sysArray);

When StoreOffsets="system", offsets are embedded in each model of the array. Call ssInterpolant(sysArray) with no offset argument.

Define SamplingGrid if one is not generated from linearize (params argument is omitted).

Multiple transfer functions (single compile) with slLinearizer:

sllin = slLinearizer(mdl);
addPoint(sllin, ["r", "y", "e", "u"]);
T = getIOTransfer(sllin, "r", "y");
S = getSensitivity(sllin, "e");
L = getLoopTransfer(sllin, "u", sign);

Stage 4: Debug (Linearization Advisor)

Use when linearization returns zero gain or unexpected results.

opts = linearizeOptions(StoreAdvisor=true);
[sys, ~, info] = linearize(mdl, io, opts);
advisorResult = advise(info.Advisor);

Always capture the output of advise — calling without an output argument launches the UI.

Inspect problematic blocks:

problematic = find(advisorResult, linqueryHasDiagnostics());
for i = 1:numel(problematic.BlockDiagnostics)
    diag = problematic.BlockDiagnostics(i);
    fprintf('%s: %s\n', diag.BlockPath, join(string(diag.DiagnosticMessages), newline));
end

Common advisor findings and resolutions:

  • "linearization has zero input/output pair" → Change operating point, or use block substitution if reasonable to do so
  • Block with hard discontinuity (PWM, relay, dead zone, non-floating point signals) → Analytical linearization will be zero. Fall back to frequency response estimation
  • Reference diagnostic message for other potential fixes

Convert if needed

tfSys = tf(sys);      % Transfer function
zpkSys = zpk(sys);    % Zero-pole-gain

LPV Validation

For LPV models, simulate and compare against Simulink:

[y, t] = lsim(lpvSys, u, tVec, x0, paramTrajectory);

Key Functions

FunctionPurposeAvailable From
linearizeLinearize Simulink modelR2006a
linearizeOptionsConfigure linearization algorithmR2006a
linioDefine linearization I/O pointsR2006a
getlinioGet I/O points defined in modelR2006a
operpointCreate operating point with manual state valuesR2006a
operspecCreate operating point specificationR2006a
findopTrim or snapshot operating pointR2006a
slLinearizerBatch/multi-transfer-function interfaceR2013b
getIOTransferClosed-loop transfer function from slLinearizerR2013b
getSensitivitySensitivity function from slLinearizerR2013b
getCompSensitivityComplementary sensitivity from slLinearizerR2013b
getLoopTransferOpen-loop transfer from slLinearizerR2013b
adviseRun linearization advisorR2017b
ssInterpolantBuild gridded LPV/LTV modelR2023a

Common Mistakes

MistakeWhy It's WrongCorrect Approach
Using linmod, linmod2, linmodv5 or dlinmodLegacy API, limited featuresUse linearize or slLinearizer
Not using advise when result is zeroLeads to trial-and-errorEnable StoreAdvisor="on", call result = advise(info.Advisor)
Using Outport/Terminator/Scope as linio pointBlocks without output ports cannot be specified as linearization I/O — errorsTrace upstream to find the source block that feeds it
opSpec(i) = opSpecBase in a loopoperspec is a handle class — this aliases, not copiesUse opArray(i) = copy(opBase)
Omitting BatchConsistency in batchState ordering may vary across operating pointsAlways set BatchConsistency="on"
Calling ssInterpolant without offsetsLPV model requires offsetsUse StoreOffsets="system"
Calling advise without output argLaunches Model Linearizer UI (hangs in non-interactive sessions)Always use result = advise(advisor)
Repeated linearize calls for different I/OsRecompiles model each timeUse slLinearizer for single compile
Manual trial-and-error for zero resultsWastes time, may not find root causeUse advisor diagnostics — identify the problematic blocks

Conventions

  • Always: Capture the output of advise() to prevent UI launch
  • Always: Use copy(opSpec) for batch operating point arrays, not assignment
  • Always: Set BatchConsistency="on" for batch linearization destined for LPV
  • Always: Set StoreOffsets="system" when building LPV models with ssInterpolant
  • Prefer: slLinearizer when extracting multiple transfer functions from one model
  • Never: Place linio on blocks without output ports (Outport, Terminator, Scope) — trace upstream to find the source block
  • Never: Use linmod, linmod2, linmodv5 or dlinmod — these are legacy

Copyright 2026 The MathWorks, Inc.


Signals

GitHub stars
1k
Forks
103
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
simulink-linearize
Source
github.com/matlab/simulink-agentic-toolkit