New Smith internal model control of two-motor drive system based on neural network generalized inverse
type: Item
instance of: scholarly article
title: New Smith internal model control of two-motor drive system based on neural network generalized inverse
zbMATH Open document ID: 1398.93266
DOI: 10.1155/2016/5794087
author: Yanyan Li
published in: Journal of Control Science and Engineering
publication date: 2018-10-08T00:00:00Z
review text: Summary: Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multi-output, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith Internal Model (SIM) control method, which is based on Neural Network Generalized Inverse (NNGI). This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93C35
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 6948707
zbMATH Keywords: MIMO nonlinear system
zbMATH Keywords: new Smith internal model control
zbMATH Keywords: two-motor drive
zbMATH Keywords: neural network
Wikidata QID: Q59126070
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/5794087
OpenAlex ID: W2292487423
description: scientific article; zbMATH DE number 6948707