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feat(estimators): add recursive instrumental variables - #352
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Recursive least squares with its gain taken along an instrument instead of the regressor. When a regressor is itself measured, least squares reads its noise as a weaker dependence on that column and converges to an attenuated coefficient. An instrument that moves with the true regressor but not with its noise, such as an injected excitation, removes that bias. With the regressor as its own instrument the recursion is the recursive least-squares one, at the same cost. e-foc uses it to identify a winding's inductance online from a derivative of measured current, with the injected d-axis current reference as the instrument: on a bench motor the least-squares estimate read the inductance 55 % low and this one reads it within 8 %. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011XTZKRSD8pDL7QvwFrVnoK
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| markdownlint | 133 | 3 | 178 | 0 | 3.22s | ||
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| ✅ YAML | yamllint | 12 | 0 | 0 | 0.63s |
Detailed Issues
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Summary
Adds
estimators::RecursiveInstrumentalVariables<T, Features>next toRecursiveLeastSquares. It is recursive least squares with the gain taken along an instrumentzinstead of the regressorx:When a regressor is itself measured, least squares converges to an attenuated coefficient (regression dilution). An instrument that moves with the true regressor but not with its noise removes that bias. A typical instrument is an injected excitation. With
z = xthe recursion is RLS, at the same O(p²) cost.Motivation
In e-foc the online winding estimator takes dI/dt from two single noisy current samples. On the bench motor the RLS estimate read the inductance 55 % low. With the injected d-axis reference as the instrument it reads within +4 to +8 % of the offline calibration, measured in 3 hardware runs. e-foc carries a local copy of this class today, on an open branch; that copy will be replaced by this one once this PR merges.
Changes
numerical/estimators/online/RecursiveInstrumentalVariables.hpp: genericTwith astatic_assertfor floating point,OPTIMIZE_FOR_SPEEDonUpdate, and anextern templatefor the coverage build.Update(regressor, instrument, y)returns the sameEstimationMetricsas RLS.RecursiveInstrumentalVariables.cpp: coverage instantiation<float, 2>.test/TestRecursiveInstrumentalVariables.cpp, twoTEST_Fs onfloat:z = xit matches RLS in coefficients and all three metrics;doc/estimators/RecursiveInstrumentalVariables.mdfollowsdoc/TEMPLATE.md, with an exact four-step walkthrough. Rows were added todoc/estimators/README.mdand to theREADME.mddocumentation table.Validation
numerical.estimators.online_test: all tests pass (host, Debug).clang-format --dry-run --Werroris clean on the new sources.Generated by Claude Code