Research on Motion Control Model of Siemens PMC System for Industrial Automation

The application of the Siemens PMC system in industrial automation is explored, with a focus on the design and enhancement of a motion control model utilizing the sliding mode control algorithm. The Siemens PMC system, a crucial automation solution, comprises multiple layers, including hardware modules, software control systems, and communication networks. The hardware components primarily handle data acquisition and the execution of control commands, while the software control system processes and analyzes the acquired data through sophisticated algorithms to achieve precise motion control. A motion control model based on the sliding mode control algorithm is subsequently developed, and its efficacy and performance improvements are validated through simulation. Experimental findings demonstrate that, compared to conventional control methods, the sliding mode control algorithm offers notable advantages in terms of system stability and precision. Specifically, motion control errors are reduced by approximately 11.8%, the system’s response time is decreased by 13.7%, and the system’s resistance to external disturbances is significantly enhanced, leading to improved overall stability. Detailed data analysis reveals that the sliding mode control algorithm not only enhances the dynamic performance of the system but also contributes to reductions in energy consumption and operational costs.

Authors

Institutions

Publication Details

Published
2024-12-29
DOI
https://doi.org/10.1109/iceace63551.2024.10898924
Primary Topic
Industrial Technology and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Motion Control Model of Siemens PMC System for Industrial Automation

Zhiqiang Zhang, Jin Liu, Xincai Chang, Xianjun Song
Industrial Technology and Control Systems
article

Research on Motion Control Model of Siemens PMC System for Industrial Automation

Zhiqiang Zhang, Jin Liu, Xincai Chang, Xianjun Song
article en

Abstract

The application of the Siemens PMC system in industrial automation is explored, with a focus on the design and enhancement of a motion control model utilizing the sliding mode control algorithm. The Siemens PMC system, a crucial automation solution, comprises multiple layers, including hardware modules, software control systems, and communication networks. The hardware components primarily handle data acquisition and the execution of control commands, while the software control system processes and analyzes the acquired data through sophisticated algorithms to achieve precise motion control. A motion control model based on the sliding mode control algorithm is subsequently developed, and its efficacy and performance improvements are validated through simulation. Experimental findings demonstrate that, compared to conventional control methods, the sliding mode control algorithm offers notable advantages in terms of system stability and precision. Specifically, motion control errors are reduced by approximately 11.8%, the system’s response time is decreased by 13.7%, and the system’s resistance to external disturbances is significantly enhanced, leading to improved overall stability. Detailed data analysis reveals that the sliding mode control algorithm not only enhances the dynamic performance of the system but also contributes to reductions in energy consumption and operational costs.

Digital China Health (China) (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 65%
Industrial Technology and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.