Skip to content

Online self-tuning of active disturbance rejection control for PMSM drives via physics-guided reinforcement learning

Sep 2026 · Nonlinear dynamics · Vol 114 · 0 citations · 35 references

TL;DR

A self-calibrating context-adaptive reward activation (CARA) mechanism is introduced, providing phase-aware guidance for transient response, disturbance rejection, and steady-state tracking in the online self-tuning of active disturbance rejection control for permanent magnet synchronous motor (PMSM) drives.

View source

Similar papers

Open access Sep 2026

Stable Offline Reinforcement Learning for Switched Reluctance Motor Drives via Multi-Demonstrator Policy Distillation

Finite-control-set model predictive control provides excellent torque–speed regulation for switched reluctance motor drives but requires an online combinatorial search at every control instant, making low-cost embedded implementation challenging. This article investigates whether offline reinforcement learning can dist...

Franklin Sánchez, M. Milanés-Montero, E. Romero-Cadaval · 0 citations
Preprint Sep 2026

Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation

A residual reinforcement learning DOB framework that pairs an analytical observer with an RL policy is proposed that derived and enforced a state-dependent action bound on the RL policy from an input-to-state stability (ISS) analysis such that the closed loop provably confines the tracking error to a certified envelope...

Jihong Kim, Joonhyuk Kwon, H. Kim et al. · 0 citations
Preprint Sep 2026

Model-Free Current Control of Permanent Magnet Synchronous Motors via ESO-Based Disturbance Feedforward and Data-Driven H-infinity Residual Feedback

This paper proposes a model-free current control method for permanent magnet synchronous motors (PMSMs) based on disturbance feedforward and residual feedback. An ultra-local current model incorporates motor dynamics, parameter uncertainties, cross-coupling effects, and other nonideal factors into generalized lumped di...

Yong-Bo Li, Shuang-Qiang Liang, Hongwei Ma · 0 citations
Open access Sep 2026

Reinforcement-Learning Robust Tracking Control of Discrete-Time Systems with Diagonal Scaling

This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...

Kan-Yang Jiang, Zheng Gao, Ye Zeng et al. · 0 citations
#machine learning Preprint Sep 2026

Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control

A runtime mechanism that grows and prunes the heads of the attention block during reinforcement learning, governed by two signals: the effective rank of the on-policy context distribution, which triggers growth when representational capacity becomes insufficient, and the per-head output magnitude, which flags redundant...

G. Cirrincione, Adriano Fagiolini · 0 citations
Preprint Aug 2026

State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U

Accurate feedback control of the plasma current ($I_p$) and centroid position $(R_c,Z_c)$ is essential for the stable operation of spherical torus (ST) plasmas. Conventional proportional-integral-derivative (PID) controllers require extensive manual tuning and struggle with the fast, strongly coupled dynamics that aris...

Peixin Guo, Zhengyuan Chen, Jianguo Chen et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.