PLC控制与Matlab Simulink仿真：翻译方法
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“PLC 控制与 MATLAB Simulink 模拟方法的研究，提出了一种 PLC 控制程序在 MATLAB/Simulink 环境中模拟的翻译方法。”
本文主要探讨了如何利用MATLAB/Simulink环境来模拟PLC（可编程逻辑控制器）的控制程序。PLC广泛应用于工业自动化领域，用于控制各种机械设备和生产过程。MATLAB/Simulink则是一个强大的系统级建模和仿真工具，常用于设计和分析动态系统。
作者Teresa Deveza和J.F. Martins提出了一种翻译方法，该方法可以自动将PLC的控制程序转化为MATLAB函数块，这个函数块能够在Simulink环境中运行，从而控制所建立的工业过程模型。这种方法的核心在于能够将不同类型的PLC、其输入/输出数量以及控制程序文件转换为与MATLAB/Simulink兼容的m文件。
具体步骤如下：
1. 输入：用户需提供PLC类型、PLC的输入和输出数目，以及PLC的控制程序文件。
2. 运行翻译包：该工具会分析并解析PLC的控制逻辑，将其转换成MATLAB函数块的语言。
3. 输出：生成一个MATLAB/Simulink兼容的m文件，包含转换后的PLC控制程序，可在Simulink环境中直接调用。
这种翻译方法的应用显著提高了教学和实践的效率。Évora大学的学生通过使用这个工具，能在Simulink中测试他们的PLC控制程序，与过程模型进行联合仿真，这不仅加深了他们对PLC控制逻辑的理解，还提供了在真实系统执行前验证控制策略的平台。
关键词：自动化、教育、工业过程控制、MATLAB、Simulink、可编程逻辑控制器、仿真、翻译方法。
通过这种方法，学生和工程师可以在模拟环境中调试和优化PLC程序，减少了在实际设备上进行实验的时间和成本。此外，它还有助于教学，使得复杂的PLC控制逻辑可以通过图形化的方式直观展现，便于学习和理解。这个翻译工具促进了理论学习与实际操作之间的桥梁，提升了工程教育的质量和实践性。
PLC Control and Matlab/Simulink Simulations.
A Translation Approach
Teresa Deveza *, J. F. Martins **
* Universidade de Évora, Évora, Portugal
** CTS/UNINOVA and FCT/UNL, Lisboa, Portugal
teresa.deveza@gmail.com; jf.martins@fct.unl.pt
Abstract — This paper proposes a translation methodology
to emulate PLC control program in the Matlab/Simulink
environment. The translation package automatically
translates the PLC control program into Matlab/Simulink
software language. The PLC control program is translated
into a Matlab function block, within the Matlab/Simulink
environment, that will control the model of the industrial
process as long as the simulation runs. The translation
package inputs are: the type of PLC, the PLC’s number of
inputs and outputs and the PLC control program file. The
translation package output will be a Matlab/Simulink
compatible m-file with the correspondent PLC control
program translation. Universidade de Évora students are
successfully using this package, so that they can test their
PLC control programs together with Matlab/Simulink
process models.
Index Terms — Automation, Education, Industrial
Control, Simulation, Modeling.
I. I
NTRODUCTION
The practical test of an automation and control process
controlled by programmable logic controllers (PLC) is a
well-known problem [1-3]. There are several solutions
that can be implemented: scale models, batteries of led’s
and switches and Human Machine Interfaces (HMI),
Supervisory Control and Data Acquisition (SCADA)
systems or simulation tools. The use of scale models of
real processes is very expensive and difficult to adapt to
different processes. There is no question that this is the
best way to teach PLC controlled process, allowing the
students to test their projects in an almost real
environment, however their cost often prohibits its use.
The use of led’s and switches sets is extremely confusing
end uninteresting. This approach, only valid when small
processes are considered, severely reduces the student’s
motivation. Some HMI and SCADA systems allow this
feature but there are very expensive, not intended for this
purpose and usually consider property protocols. Some
PC-based process simulation tools have been developed,
using microcontroller technologies and designed to work
with any type of PLC [4]. Other commercial PLC
simulation tools are also available (PC-SIM, SIMTSX,
PSIM just to name a few). However, often these solutions
are not suitable to be integrated with other simulation
tools.
The use of Matlab/Simulink [5] has not been a regular
approach for teaching industrial automation and PLC
controlled processes. Assuming that the model of the
industrial process is implemented in the Matlab/Simulink
this paper presents a tool that can be used to implement
the PLC control program in Matlab/Simulink
environment. The basic idea is to consider the PLC
control program as a Matlab function block, within the
Matlab/Simulink environment, that will control the model
of the industrial process as long as the simulation runs.
The main objective of this work is to automatically
translate the PLC control program, written as an
instruction list, into Matlab/Simulink software language.
II. I
NDUSTRIAL PROCESS MODELLING
The implementation of an industrial production line
involves a large investment. Every decision at the design
stage should be made with extremely care in order to
assure that the automated manufacturing process will
successfully achieve the desired results.
The problem of industrial process modeling is not easy
or unique, and several approaches can be taken. These
processes can be considered as a discrete event system,
where the states of the dynamic system involved changes
accordingly to the occurrences of various and distinct
events. It is important, to keep the manufactures and
process integrators competitiveness, that the industrial
production systems keep being constantly improved. To
achieve this objective an efficient modeling approach is a
fundamental issue.
A modern production line is a highly integrated system
composed of automated workstations such as robots with
tool-changing capabilities, a hardware handling system
and storage system, PLC controlled processes, and a
computer control system that controls the operations of
the entire system. Every controlled industrial process can
be modeled as a transfer function (continuous and/or
discrete) with a set of inputs and a set of outputs, as
presented in Fig. 1. The inputs refer to the control signals
that are applied to the process actuators, and the outputs
refer to the variables that are acquired through the process
network of sensors and detectors.
978-1-4244-2728-4/09/$25.00 ©2009 IEEE
Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on December 17, 2009 at 10:43 from IEEE Xplore. Restrictions apply.
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