Yazar "Jones, AH" seçeneğine göre listele

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    Discrete event control system design using automation Petri nets and their ladder diagram implementation
    (SPRINGER LONDON LTD, 1998) Uzam, M; Jones, AH
    As automated manufacturing systems become more complex, the need for an effective design tool to produce both high-level discrete event control systems (DECS) and low-level implementations becomes more important. Petri nets represent the most effective method for both the design and implementation of DECSs. In this paper automation Petri nets (APN) are introduced to provide a new method for the design and implementation of DECSs. The APN is particularly well suited to multiproduct systems and provides a more effective solution than Grafcet in this context. Since ordinary Petri nets do not deal with sensors and actuators of DECSs, the Petri net concepts are extended, by including actions and sensor readings as formal structures within the APN. Moreover enabling and inhibitor nrcs, which can enable or disable transitions through the use of leading-edge, falling-edge and level of markings, are also introduced. Tn this paper the methodology is explained by considering a fundamental APN structure. The conversion of APNs into the IEC1131-3 ladder diagrams (LD) for implementation on a PLC is also explained by using the token passing logic (TPL) concept Finally, an illustrative example of how APNs can be applied to a discrete manufacturing problem is described in detail.
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    Neurovision-based logic control of an experimental manufacturing plant using neural net le-net5 and automation Petri nets
    (SPRINGER, 2005) Karlik, B; Uzam, M; Cinsdikici, M; Jones, AH
    In this paper, Petri nets and neural networks are used together in the development of an intelligent logic controller for an experimental manufacturing plant to provide the flexibility and intelligence required from this type of dynamic systems. In the experimental setup, among deformed and good parts to be processed, there are four different part types to be recognised and selected. To distinguish the correct part types, a convolutional neural net le-net5 based on-line image recognition system is established. Then, the necessary information to be used within the logic control system is produced by this on-line image recognition system. Using the information about the correct part types and Automation Petri nets, a logic control system is designed. To convert the resulting Automation Petri net model of the controller into the related ladder logic diagram (LLD), the token passing logic (TPL) method is used. Finally, the implementation of the control logic as an LDD for the real time control of the manufacturing system is accomplished by using a commercial programmable logic controller (PLC).
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    Using a Petri-net-based approach for the real-time supervisory control of an experimental manufacturing system
    (SPRINGER LONDON LTD, 2000) Uzam, M; Jones, AH; Yucel, I
    A new Petri-net-based design technique, called the inhibitor arc method, for the synthesis of compiled supervisors for discrete event systems is used to solve a forbidden state problem in an experimental manufacturing system. The technique used offers the following advantages: 1. The closed-loop (i.e. controlled) behaviours of the systems are non-blocking and do not contradict the forbidden state specifications. 2. The closed-loop behaviours of the systems are maximally permissive within the specifications. The supervisors to be synthesised consist of a controlled automation Petri net (APN) model of the system. Automation Petri nets include the following extensions to the ordinary Petri-net framework: sensor readings as firing conditions at transitions and actions assigned to places. Ladder logic diagram (LLD) code is used to implement the supervisors on programmable logic controllers (PLC). It is important to note that the supervisors obtained are correct by construction, therefore there is no need for verification. This paper particularly shows the applicability of previous results [1] to low-level real-time control where the role of the supervisor is to arrange low-level interaction between the control devices, such as motors, actuators, etc. This is done by considering an experimental manufacturing system.