scholarly journals Cooperation of agents in complex systems based on supervision

2014 ◽  
Vol 14 (1) ◽  
pp. 40-51
Author(s):  
František Čapkovič
Keyword(s):  
Complex Systems ◽  
State Vector ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
The State ◽  
System Structure ◽  
Continuous Systems ◽  
Production Lines ◽  
Supervisor Synthesis

Abstract Complex systems consist of many cooperating devices. To have a transparent view on the system structure, as well as on the structural interconnections and cooperation of the subsystems, it is useful to synthesize the complex systems systematically in a prescribed order, even in analytical terms (if possible). The supervision of the subsystems seems to be a very suitable approach to accomplish these demands, and consequently it makes the complex systems diagnostics easier. The substantial agents (i.e., the agents of material nature − e.g., devices like particular production lines, robots, numerically controlled machines, etc.) can be coordinated and forced to cooperation by means of efficiently synthesized supervisors. The cooperation process has the character of DES (Discrete-Event Systems), because any system (including continuous systems), has minimally two discrete states - idle and working. DES control theory can be successfully utilized in supervisor synthesis. There are several approaches to modeling the agents and the process of supervisor synthesis. The Petri net-based approach is one of them. Place/Transition Petri Nets (P/T PN) are used here for modeling the behaviour of particular agents, as well as in the computational parocess used for the supervisor synthesis. Two main methods of the P/T PN-based supervision will be used, namely (i) the supervision based on the place invariants (P-invariants) of P/T PN, utilizing only the state vector during the supervisor synthesis, and (ii) the extended supervision utilizing not only the state vector, but also the control vector and Parikh’s vector. The efficiency of the proposed approach is illustrated in a case study.

scholarly journals State Feedback of Complex Systems Using Fuzzy Cognitive Maps

2018 ◽  
Vol 6 (3) ◽  
pp. 1-6 ◽  
Author(s):  
Vassiliki Mpelogianni ◽  
Ioannis Arvanitakis ◽  
Peter Groumpos
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
State Space ◽  
State Vector ◽  
State Feedback ◽  
Mathematical Formulation ◽  
Cognitive Maps ◽  
The State ◽  
Fuzzy Cognitive Maps ◽  
System Model

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems.

scholarly journals Evaluation and comparison of a lean production system by using SAE J4000 standard: a case study on the automotive industry in the state of Mexico

2017 ◽  
Vol 14 (4) ◽  
pp. 461
Author(s):  
Lina María Tabares
Keyword(s):  
Data Analysis ◽  
Automotive Industry ◽  
Production System ◽  
Lean Manufacturing ◽  
The State ◽  
Descriptive Statistics ◽  
Production Lines ◽  
Reducing Costs ◽  
Implementation Level

Lean manufacturing (LM) is a management system focused on eliminating waste and activities that do not add value, with the aim of reducing costs and improving the quality and productivity of organizations. LM has been adopted in diverse industries and several countries due to its advantages in cost, flexibility and rapid response (Muslimen et al., 2013). The objective of this investigation is to analyze the implementation level to the Lean System via the SAE J4000 (SAE 1999a) standard carried out among companies of the State of Mexico automotive industry. In addition, this investigation shows the inferential and descriptive statistics data analysis of Mexican companies compared to the automotive industries in Spain and Brazil. Results show that the implementation level of the automotive industry is at 48.4% in the State of Mexico according to the SAE J4000 standard. Moreover, the involvement of suppliers and the use of lean tools in processes are higher in the State of Mexico compared to automotive industries in Spain and Brazil. However, previous studies ranked the State of Mexico at a lower level of LM in contrast with Spain and Brazil production lines.

Measurement of Effectiveness for an Anti-torpedo Combat System Using a Discrete Event Systems Specification-based Underwater Warfare Simulator

2011 ◽  
Vol 8 (3) ◽  
pp. 157-171 ◽  
Author(s):  
Kyung-Min Seo ◽  
Hae Sang Song ◽  
Se Jung Kwon ◽  
Tag Gon Kim
Keyword(s):  
Discrete Event Systems ◽  
Statistical Evaluation ◽  
Discrete Event ◽  
Underwater Vehicle ◽  
Decision Making Process ◽  
Sensor Model ◽  
Event Systems ◽  
Simulation Based ◽  
System Effectiveness

Modeling and simulation (M&S) has long played an important role in developing tactics and evaluating the measure of effectiveness (MOE) for the underwater warfare system. In simulation-based acquisition, M&S technology facilitates decisions about future equipment procurements, such as a mobile decoy or a torpedo. In addition, assessment of submarine tactical development, during an engagement against a torpedo, can be conducted using M&S techniques. This paper presents a case study that applies discrete event systems specification-based M&S technology to develop a simulation of an underwater warfare system, specifically, an anti-torpedo combat system, to analyze the MOE of the system. The entity models required for M&S are divided into three sub-models: controller, maneuver, and sensor model. The developed simulation allows us to conduct a statistical evaluation of the overall underwater warfare system under consideration, an assessment of the anti-torpedo countermeasure’s effectiveness, and an assessment of tactics development of the underwater vehicle. Moreover, it can be utilized to support the decision-making process for future equipment procurements. In order to analyze the system effectiveness, we performed extensive combat experiments by varying parameters, such as various tactics and weapon performance. The experimental results show how the factors influence the MOEs of the underwater warfare system.

scholarly journals A DECENTRALIZED MODEL-BASED DIAGNOSTIC TOOL FOR COMPLEX SYSTEMS

2002 ◽  
Vol 11 (03) ◽  
pp. 327-346 ◽  
Author(s):  
Y. PENCOLÉ ◽  
M.-O. CORDIER ◽  
L. ROZÉ
Keyword(s):  
Complex Systems ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Global Model ◽  
Telecommunication Networks ◽  
Model Based ◽  
On Line ◽  
The Impact ◽  
Decentralized Model ◽  
Merging Strategy

We address the problem of diagnosing complex discrete-event systems such as telecommunication networks. Given a flow of observations from the system, the goal is to explain those observations by identifying and localizing possible faults. Several model-based diagnosis approaches deal with this problem but they need the computation of a global model which is not feasible for complex systems like telecommunication networks. Our contribution is the proposal of a decentralized approach which permits to carry out an on-line diagnosis without computing the global model. This paper describes the implementation of a tool based on this approach. Given a decentralized model of the system and a flow of observations, the program analyzes the flow and computes the diagnosis in a decentralized way. The impact of the merging strategy on the global efficiency is demonstrated and illustrated by experimental results on a real application.

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