Parallel adaptive RBF neural network-based active disturbance rejection control for hybrid compensation of PMSM

Abstract

Purpose

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

Design/methodology/approach

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Findings

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

Originality/value

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

Citation

Gao, P., Su, X., Pan, Z., Xiao, M. and Zhang, W. (2024), "Parallel adaptive RBF neural network-based active disturbance rejection control for hybrid compensation of PMSM", Robotic Intelligence and Automation, Vol. 44 No. 5, pp. 658-667. https://doi.org/10.1108/RIA-03-2023-0036