scholarly journals MODELING PREDICTIVE TRACKING CONTROL FOR MAX-PLUS LINEAR SYSTEMS IN MANUFACTURING

2020 ◽  
Vol 3 (2) ◽  
pp. 133-147
Author(s):  
Lathifatul Aulia ◽  
Widowati Widowati ◽  
R. Heru Tjahjana ◽  
Sutrisno Sutrisno
Keyword(s):  
Linear Systems ◽  
Production System ◽  
Tracking Control ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Process Flow ◽  
Sequence Of Events ◽  
Input And Output ◽  
Event System ◽  
Event Systems

Discrete event systems, also known as DES, are class of system that can be applied to systems having an event that occurred instantaneously and may change the state. It can also be said that a discrete event system occurs under certain conditions for a certain period because of the network that describes the process flow or sequence of events. Discrete event systems belong to class of nonlinear systems in classical algebra. Based on this situation, it is necessary to do some treatments, one of which is linearization process. In the other hand, a Max-Plus Linear system is known as a system that produces linear models. This system is a development of a discrete event system that contains synchronization when it is modeled in Max-Plus Algebra. This paper discusses the production system model in manufacturing industries where the model pays the attention into the process flow or sequence of events at each time step. In particular, Model Predictive Control (MPC) is a popular control design method used in many fields including manufacturing systems. MPC for Max-Plus-Linear Systems is used here as the approach that can be used to model the optimal input and output sequences of discrete event systems. The main advantage of MPC is its ability to provide certain constraints on the input and output control signals. While deciding the optimal control value, a cost criterion is minimized by determining the optimal time in the production system that modeled as a Max-Plus Linear (MPL) system. A numerical experiment is performed in the end of this paper for tracking control purposes of a production system. The results were good that is the controlled system showed a good performance.

scholarly journals Identification of Stochastic Timed Discrete Event Systems with st-IPN

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Doyra Mariela Muñoz ◽  
Antonio Correcher ◽  
Emilio García ◽  
Francisco Morant
Keyword(s):  
Petri Net ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Input Output ◽  
Identification Process ◽  
Sequence Of Events ◽  
Input And Output ◽  
Event Systems ◽  
Systems Modelling ◽  
Stochastic Time

This paper presents a method for the identification of stochastic timed discrete event systems, based on the analysis of the behavior of the input and output signals, arranged in a timeline. To achieve this goal stochastic timed interpreted Petri nets are defined. These nets link timed discrete event systems modelling with stochastic time modelling. The procedure starts with the observation of the input/output signals; these signals are converted into events, so that the sequence of events is the observed language. This language arrives to an identifier that builds a stochastic timed interpreted Petri net which generates the same language. The identified model is a deterministic generator of the observed language. The identification method also includes an algorithm that determines when the identification process is over.

scholarly journals Analysis and control of max-plus linear discrete-event systems: An introduction

2019 ◽  
Vol 30 (1) ◽  
pp. 25-54 ◽  
Author(s):  
Bart De Schutter ◽  
Ton van den Boom ◽  
Jia Xu ◽  
Samira S. Farahani
Keyword(s):  
Model Predictive Control ◽  
Linear Systems ◽  
Predictive Control ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Specific Class ◽  
Event Systems ◽  
Basic Concepts ◽  
And Control

AbstractThe objective of this paper is to provide a concise introduction to the max-plus algebra and to max-plus linear discrete-event systems. We present the basic concepts of the max-plus algebra and explain how it can be used to model a specific class of discrete-event systems with synchronization but no concurrency. Such systems are called max-plus linear discrete-event systems because they can be described by a model that is “linear” in the max-plus algebra. We discuss some key properties of the max-plus algebra and indicate how these properties can be used to analyze the behavior of max-plus linear discrete-event systems. Next, some control approaches for max-plus linear discrete-event systems, including residuation-based control and model predictive control, are presented briefly. Finally, we discuss some extensions of the max-plus algebra and of max-plus linear systems.

scholarly journals Equivalent and Efficient Optimization Models for an Industrial Discrete Event System with Alternative Structural Configurations

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Juan-Ignacio Latorre-Biel ◽  
Emilio Jiménez-Macías ◽  
Mercedes Pérez de la Parte
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Discrete Event System ◽  
Optimization Techniques ◽  
Event System ◽  
Event Systems ◽  
Structural Configurations ◽  
Computational Resources

Discrete event systems in applications, such as industry and supply chain, may show a very complex behavior. For this reason, their design and operation may be carried out by the application of optimization techniques for decision making in order to obtain their highest performance. In a general approach, it is possible to implement these optimization techniques by means of the simulation of a Petri net model, which may require an intensive use of computational resources. One key factor in the computational cost of simulation-based optimization is the size of the model of the system; hence, it may be useful to apply techniques to reduce it. This paper analyzes the relationship between two Petri net formalisms, currently used in the design of discrete event systems, where it is usual to count on a set of alternative structural configurations. These formalisms are a particular type of parametric Petri nets, called compound Petri nets, and a set of alternative Petri nets. The development of equivalent models under these formalisms and the formal proof of this equivalence are the main topics of the paper. The basis for this formal approach is the graph of reachable markings, a powerful tool able to represent the behavior of a discrete event system and, hence, to show the equivalence between two different Petri net models. One immediate application of this equivalence is the substitution of a large model of a system by a more compact one, whose simulation may be less demanding in the use of computational resources.

scholarly journals DECIDABILITY AND COMPLEXITY ANALYSIS OF FORBIDDEN STATE PROBLEMS FOR DISCRETE EVENT SYSTEMS

2008 ◽  
Vol 19 (04) ◽  
pp. 999-1013 ◽  
Author(s):  
HSU-CHUN YEN
Keyword(s):  
Discrete Event Systems ◽  
Complexity Analysis ◽  
Discrete Event ◽  
Control Policy ◽  
State Problem ◽  
Event System ◽  
Event Systems ◽  
Complexity Issue ◽  
Finite State ◽  
Decidability And Complexity

The conventional forbidden state problem for discrete event systems is concerned with the issue of synthesizing a maximally permissive control policy to prevent a discrete event system from reaching any forbidden state during the course of its computation. In this paper, we regard the forbidden state problem as a decision problem, and investigate the decidability/complexity issue of the problem under two new types of control policies, namely, non-blocking and fair policies, for finite state systems and Petri nets.

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