RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

Zhi Liu,Tefang Chen

doi:10.1155/2016/8390529

Abstract

Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.

Highlights

The most common electrohydraulic actuator is the multistage hydraulic actuator with the nozzle flapper pilot [1]
Because the conventional method could not meet the design requirement, the parameters of the position control are first adjusted by the Radial Basis Function (RBF) neural network automatically, and the hydraulic disturbance is estimated by the extended state observer (ESO)
The results of the PID control, the RBF neural network PID control (RBF-PID), and the RBF neural network control based on the extended state observer (RBF-ESO) were compared

Summary

Introduction

The most common electrohydraulic actuator is the multistage hydraulic actuator with the nozzle flapper pilot [1]. The classic linear PID controller has an unavoidable long recovery process and an inevitable error when the system reaches a stable state [10, 11] In this case, the classic PID control strategy could not get fast and accurate position response. The literature [12] reported a control scheme based on disturbance observer to realize motion control with high accuracy This strategy uses low-pass filter so the performance of the actuator would be influenced while tracking the high-frequency signal. Artificial neural network [14, 15] has the advantages of distributed storage, parallel processing, nonlinear approximation, and self-learning It shows a broad application prospect in the field of linear servo control.

Modeling of Direct Drive Actuator System with Linear Motor

The Proposed Control Scheme

Experiments

Findings

Conclusions