# Online Multi-Parameter Identification for PMSM Parameter Monitoring Based on a ZOH Model and Dual-Sampling Strategy

## TL;DR

A new method improves the accuracy of identifying parameters in permanent magnet synchronous motors using advanced discretization and voltage compensation techniques.

## Contribution

A novel online multi-parameter identification method for PMSMs using ZOH discretization and dual-sampling strategy with voltage compensation.

## Key findings

- The proposed ZOH discretization reduces model errors, especially at high speeds.
- Dual-sampling and d-axis injection overcome rank deficiency in identification matrices.
- The method achieves low identification errors for resistance, inductance, and flux linkage under high-speed conditions.

## Abstract

The accuracy of online parameter identification for permanent magnet synchronous motors (PMSMs) is constrained by discrete model errors, rank deficiency in the steady-state identification matrix, and voltage deviations resulting from inverter nonlinearities. This paper proposes a multi-parameter identification method acting as a high-precision virtual sensor, based on Zero-Order Hold (ZOH) discretization and an inverter nonlinear voltage compensation scheme utilizing a dual-sampling strategy. First, a discrete model of the PMSM, accounting for rotor position variations within the control period, is established using the ZOH discretization method. Compared with the forward Euler discretization method, this approach effectively minimizes discretization model errors, especially under high-speed operating conditions where rotor position variations are significant. Second, the rank deficiency problem of the steady-state identification matrix is overcome by combining d-axis small-signal injection with a dual-sampling strategy. Furthermore, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is introduced to achieve online multi-parameter identification. Finally, the influence mechanisms of the dead-time effect, power switch voltage drop, and turn-on delay on the output voltage are analyzed. Consequently, an inverter nonlinear voltage compensation strategy tailored for the dual-sampling mode is proposed. Experimental results demonstrate that the proposed method significantly enhances parameter identification accuracy across the entire speed range. Specifically, under high-speed conditions, the identification errors for resistance, inductance, and flux linkage are maintained within 5.47%, 4.05%, and 2.46%, respectively.