Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.39840

Parameter-Robust Sensorless Control of IPMSM Drives With Adaptive Flux Observer

Fobao Zhou·Zhenxiao Yin·Xueyan Wang·Yang Shen·Yuanfeng Qu·Hang Zhao
View original source ↗Read source document ↗Export metadata ↓

Abstract

To address parameter sensitivity commonly in interior permanent magnet synchronous motor (IPMSM) sensorless control, this paper proposes a parameter-robust control framework by extending the adaptive flux observer from surface-mounted PMSM to salient-pole machines. First, parameter mismatches are interpreted as an equivalent flux vector with $d$-axis and $q$-axis components. Permanent magnet flux, $d$-axis inductance, and resistance mismatches are mapped to the $d$-axis flux component, whereas $q$-axis inductance mismatch is mapped to the $q$-axis component. Then, a scalar adaptive flux update is designed to compensate the $d$-axis flux variation by tracking the equivalent flux magnitude. Since the $q$-axis component rotates the observed flux direction and cannot be eliminated by scalar adaptation, a high-frequency $q$-axis voltage injection method is introduced to identify the $q$-axis inductance. Meanwhile, stability analysis proves bounded equivalent flux magnitude tracking and identifies the residual tangential flux responsible for position error. Finally, experimental results show that the proposed method does not require precise resistance, $d$-axis inductance, or flux, while the $q$-axis inductance is provided by online identification.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fobao Zhou, Zhenxiao Yin, Xueyan Wang, Yang Shen, Yuanfeng Qu, Hang Zhao. 2026-09-30. Parameter-Robust Sensorless Control of IPMSM Drives With Adaptive Flux Observer. https://arxiv.org/abs/2609.39840

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Numerical Investigation of Indirect Adaptive Predictive Control with Structure-Informed Nonlinear Regressors

This paper numerically investigates nonlinear model construction from supplied structural information within predictive cost adaptive control. The study varies the retained nonlinear functions, coefficient sharing, and forward reconstruction under a common identification and control architecture. Recursive least squares updates unknown coefficients from direct or integrated relations that are linear in the parameters. The reconstructed nonlinear forward predictor need not be parameter-linear. A local affine approximation, constructed from the predictor value and Jacobian, is frozen over the prediction horizon for constrained quadratic optimization. Scalar full-state studies compare exact parameterizations, Taylor-informed approximations, broader dictionaries, and incorrectly restricted models. The comparisons include parameter variation, structural changes, and selected noisy cases. Common-data tests assess nonlinear prediction, whereas common-query forecasts distinguish nonlinear-model error from affine-freezing error. Under the tested conditions, correct structure improves prediction relative to affine models without increasing parameter count. Integrated physical identification reduces prediction error relative to direct sampled-map fitting after initialization. Additional coefficients and improved nonlinear prediction do not ensure lower tracking error. Forgetting improves readaptation without ensuring parameter recovery, and coefficient activation does not establish structure discovery. Affine freezing can reverse the nonlinear prediction advantage over an affine baseline. The results support evaluating representation, identification, reconstruction, and horizon prediction separately before increasing model complexity.

eess.SY↗
arXiv2026

Beyond Tomorrow -- Electrification of Hard-to-Abate Sectors

Reaching net-zero greenhouse-gas (GHG) emissions requires deep electrification beyond ground transportation, buildings, and conventional industry automation. This article discusses direct electrification pathways for hard-to-abate sectors, which today account for 30% of global GHG emissions and include steel, cement, chemicals, aviation, and shipping. Whereas indirect electrification via hydrogen and synthetic fuels will remain indispensable for some applications, direct use of electricity is generally preferable from an efficiency perspective. We review selected emerging opportunities for power electronics in industrial process electrification, including steelmaking through hydrogen plasma smelting reduction or molten-oxide electrolysis, electrified cement production, high-temperature process heat, plasma-assisted chemistry, and electrochemical synthesis. Across these examples, power converters must interface with highly application-specific loads, ranging from low-voltage high-current electrolysis cells to dynamic plasma reactors and megawatt-scale induction heating systems. In addition, high-power grid interfaces and in-plant energy management/buffering solutions must be clarified. Thus, future electrified industrial processes open new "beyond-tomorrow" research challenges for power electronics in close interdisciplinary collaboration with scientists from neighboring disciplines.

eess.SY↗
arXiv2026

On the NP-Hardness of Unconstrained Static Output Feedback Stabilization

In this paper we give a polynomial-time reduction from the Subset Sum Problem to unconstrained static output feedback stabilization, establishing its NP-hardness. The construction uses two scalar building blocks to impose approximate discrete choices and a weighted-sum constraint. Once the problem is encoded, we relate the stability of the $N+1$ decoupled loops encoding the problem to that of a coupled plant. This is accomplished by leveraging frequency separation via a band-pass transformation, and comparing the root counts of the true characteristic polynomial with that of the different blocks in different regions of the right half-plane. The construction then naturally bounds every stabilizing gain and gives explicit modulus-margin estimates, ensuring that the plant data have polynomial binary encoding length.

eess.SY↗