2026-07-21 · 6 min read · Rahul Chitturi
- matlab
- simulink
- review
Engineering teams often keep calibration parameters in JSON because it is easy to review, generate, and share with backend services. Simulink masks and MATLAB scripts usually need struct assignments with valid field names, consistent units, and arrays in the expected shape.
Manual conversion is risky when a model update changes one nested parameter. A pasted value may run without syntax errors while using an old default, transposed lookup table, or field name that no longer matches the mask script.
Validate structure before conversion
Start with the JSON source of truth. Formatting the file makes nested parameters and arrays easier to inspect before converting them into MATLAB syntax.
- Confirm every JSON key maps to a valid MATLAB struct field name
- Check units and scaling comments before copying numeric values
- Verify vector orientation and matrix dimensions for lookup tables
- Diff the new parameter set against the version currently used by the model
Keep review artifacts safe
If calibration data is proprietary, share reduced fixtures that preserve shape and field names without exposing production values. The review should still prove that nesting, defaults, arrays, and unit conversions behave as expected.
When a mask script initializes derived parameters, include those assumptions in the review note. The JSON may be correct, but the MATLAB initialization path can still override or transform values after paste.
A Flashman workflow
Use the JSON formatter to inspect the source, the MATLAB struct converter for assignment syntax, the MATLAB array helper for matrices, and the diff tool to compare old and new parameter sets.
End with a short checklist in the model change: field names valid, units reviewed, array shapes confirmed, defaults intentional, and generated MATLAB syntax tested with non-sensitive fixtures.