Design of industrial automated systems via relay ladder logic programming and petri nets

Document Type

Article

Publication Date

12-1-1998

Abstract

For the past decades, programmable logic controllers (PLC's) using relay ladder logic (RLL) programming have been the workhorse for controlling event-driven industrial automated systems. RLL proved flexible compared to the hardwired RLL control implementation, due to its feature of software implementation. As automated systems become more complex, they also become more difficult to understand and maintain. It takes tremendous effort to accommodate specification changes, which become frequent, to meet today's flexible and agile automation needs. Several methods emerge to overcome the shortcomings of RLL. Petri nets (PN's), initially proposed as a modeling tool, have been developed as such a method. This paper adopts an industrial scale system to compare RLL and PN design methods so that the advantages of PN-like approaches are fully recognized. The criteria are the understandability that relates to the ability to evaluate the programmed logic, to verify its correctness, and to maintain the control system as well as the flexibility that relates to the easy modification of logic when the specification changes. To the authors' knowledge, this is the first work that takes an existing industrial system, conducts discrete event control designs by using both RLL programming and PN methods, and performs a comparative study on them. Together with the previous comparison results through small-scale systems, the results of this study support that PN-like advanced discrete event control design methods are better than RLL, in terms of the understandability and flexibility of a resulting control design. © 1998 IEEE.

Identifier

0032001865 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews

External Full Text Location

https://doi.org/10.1109/5326.661096

ISSN

10946977

First Page

137

Last Page

150

Issue

1

Volume

28

Grant

DMI-940386

Fund Ref

National Science Foundation

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