Discrete Event System Theory Research Articles

Statistical comparison of Path-Complete Lyapunov Functions: a Discrete-Event Systems perspective

The goal of this paper is to advertise the tight links between the theory of Discrete-Event Systems and Path-Complete Lyapunov Functions. These are algebro-combinatorial stability criteria for hybrid systems, whose meta-parameters are an automaton and a template of candidate Lyapunov functions.To do this, we analyse a phenomenon recently observed in the literature, namely the statistical ordering of Path-Complete Lyapunov Functions, which is by far not well understood yet: Path-Complete Lyapunov functions can be endowed with a preorder structure with respect to their performance. It has been recently shown that the preorder corresponding to relative worst case performance can be characterized with tools from automata theory, but the ordering corresponding to statistical relative performance has remained elusive. We advocate for a Discrete-Event Systems approach to this problem and provide preliminary results in this direction.

Synthesis of winning attacks on communication protocols using supervisory control theory: two case studies

There is an increasing need to study the vulnerability of communication protocols in distributed systems to malicious attacks that attempt to violate properties such as safety or nonblockingness. In this paper, we propose a common methodology for formal synthesis of successful attacks against two well-known protocols, the Alternating Bit Protocol (ABP) and the Transmission Control Protocol (TCP), where the attacker can always eventually win, called For-all attacks. This generalizes previous work on the synthesis of There-exists attacks for TCP, where the attacker can sometimes win. We model the ABP and TCP protocols and system architecture by finite-state automata and employ the supervisory control theory of discrete event systems to pose and solve the synthesis of For-all attacks, where the attacker has partial observability and controllability of the system events. We consider several scenarios of person-in-themiddle attacks against ABP and TCP and present the results of attack synthesis using our methodology for each case.

Distributed supervisory control for multiple robot autonomous navigation performing single-robot tasks

This paper proposes two new approaches based on the Supervisory Control Theory (SCT) of Discrete Event Systems (DES) for autonomous navigation of multiple robots with single-robot tasks being assigned by a centralized scheduler. The two planning strategies differ regarding their implementation, which can be centralized or distributed. Nevertheless, both approaches share the central objective of considering the scheduler and robots as DES, allowing the use of SCT to model and control the behavior of the whole multi-robotic system. Particularly, SCT is used to gather deliberative and reactive motion planning algorithms through a structured procedure, aiming to safely navigate in cluttered, dynamic, partially-known environments. The open-loop behavior of the autonomous navigation system is modeled for both approaches. Moreover, standard supervisors are obtained for the centralized planning approach. In addition, the supervisor localization method is considered to allow a distributed planning approach. Furthermore, as a case study, real experiments considering mobile robots are carried out to corroborate the proposed framework.

DES-Based Optimization of Hospital Consultation and Treatment Process under the COVID-19

To solve the problem of hospital queues during the COVID-19, this project takes The Second Hospital, University of South China as an example, clarifies the hospital outpatient process under the epidemic through on-site research and data analysis, collects the waiting time and service time of patients at each stage, builds its simulation model based on Discrete Event System (DES) theory, and discovers through simulation experiments, bottlenecks limiting the efficiency of the process were identified, and a service improvement plan was proposed through process optimization.

Autonomous Operation of the DC Fast-Charging Station

This article develops, evaluates, and verifies a supervisory controller (SC) to transfer from the grid-connected mode and operate a battery-enhanced electric vehicles’ (EVs) dc fast charging (DCFC) station in the autonomous mode, when the supply grid is not available. The SC is designed based on the supervisory control theory (SCT) of discrete event systems and is based on a rigorous mathematical process; nonblocking, i.e., avoids entering an operational deadlock scenario that leads to the system collapse; minimally restrictive with respect to the station's discrete behavior; modular; and scalable. The SC also operates the station in the grid-connected mode and provides a seamless transition between the two modes and thus the DCFC station is also synonymous with a dc-microgrid. The SC is implemented on an industrial programmable logic controller and its performance is verified in a real-time hardware-in-the-loop environment using an OPAL-RT testbed.

Application of networked discrete event system theory on intelligent transportation systems

The responses of vehicles to the changes in traffic situations inevitably have delays in observing an event and implementing a control command, which often causes fatal accidents. So far, the methods for handling delays are empirical and cannot be mathematically proven. To eliminate the accidents caused by such delays, in this paper, we develop mathematically provable methods to handle these delays. Specifically, we use networked discrete event systems to model the process of driving vehicles and present a supervisory controller for handling delay situations. The method developed in this paper could serve as a new start for modeling and controlling the responsive behaviors of self-driving vehicles in the future.

Incremental Improvements of Heuristic Policies for Average-Reward Markov Decision Processes

Abstract Within the realm of Discrete Event Systems (DES) theory, the problem of performance optimization for many applications can be modeled as an infinite-horizon, average-reward Markov Decision Process (MDP) with a finite state space. In principle, these MDPs can be solved by various well-developed methods like value iteration, policy iteration and linear programming. But in reality, the tractability of these methods in the context of the aforementioned applications is compromised by the explosive size of the underlying state spaces, a problem that is known as “the curse of dimensionality”. Hence, the corresponding performance optimization problems are frequently addressed by heuristic control policies. The considered work uses results from (i) the sensitivity analysis of Markov reward processes and (ii) the ranking & selection theory in statistics in order to develop a methodology for assessing the optimality of isolated decisions in the context of any well-defined heuristic control policy for the aforementioned MDPs. It also determines an improved decision when the current one is found to be suboptimal. Hence, when embedded in an iterative scheme, this methodology can support the incremental enhancement of the original heuristic policy in a way that controls, both, the computational and also the representational complexity of the new policy. Finally, an additional important feature of the presented methodology is that it can be executed either in an “off-line” mode, using a simulation of the dynamics of the underlying DES, or in an “on-line” mode, based on the sample path that is defined by the real-time dynamics of the controlled system.

Control of Black-Box Embedded Systems by Integrating Automaton Learning and Supervisory Control Theory of Discrete-Event Systems

The paper presents an approach to the control of black-box embedded systems by integrating automaton learning and supervisory control theory (SCT) of discrete-event systems (DES), where automaton models of both the system and requirements are unavailable or hard to obtain. First, the system is tested against the requirements. If all the requirements are satisfied, no supervisor is needed and the process terminates. Otherwise, a supervisor is synthesized to enforce the system to satisfy the requirements. To apply SCT and automaton learning technologies efficiently, the system is abstracted to be a finite-discrete model. Then, a $C^{*}$ learning algorithm is proposed based on the classical $L^{*}$ algorithm to infer a Moore automaton describing both the behavior of the system and the conjunctive behavior of the system and the requirements. Subsequently, a supervisor for the system is derived from the learned Moore automaton and patched on the system. Finally, the controlled system is tested again to check the correctness of the supervisor. If the requirements are still not satisfied, a larger Moore automaton is learned and a refined supervisor is synthesized. The whole process iterates until the requirements hold in the controlled system. The effectiveness of the proposed approach is manifested through two realistic case studies. Note to Practitioners —Supervisory control theory of DES can synthesize maximally permissive supervisory controllers to ensure the correctness of software-controlled processes. The application of supervisory control theory relies on automaton models of the plant and specifications; however, the required models are often unavailable and difficult to obtain for black-box embedded systems. Automaton learning is an effective method for inferring models of black-box systems. This paper integrates the two technologies so that the supervisory control theory is applicable to the development of black-box embedded software systems. The proposed approach is implemented in a toolchain that connects automaton learning algorithms, SCT, and testing algorithms via scripts. The obtained supervisor is implemented as a software patch to monitor and control the original system online.

Online Self-Learning Fuzzy Discrete Event Systems

The fuzzy discrete event system theory is unique in that it is capable of modeling a class of event-driven systems as fuzzy automata with states and event-invoked state transitions being ambiguous. At present, the theory lacks a self-learning component, an important topic that has hardly been touched in the literature. In this article, we use stochastic gradient descent to develop online learning algorithms for the fuzzy automata. We uncover an inherent obstacle in the initial derived algorithms that fundamentally restricts their learning capability owing to dependences of the model parameters to be learned. We develop a novel mechanism to not only overcome the obstacle but also make the learning adaptive. Our final algorithms can learn an event transition matrix based on automaton's states before and after the occurrence of a fuzzy event, and learn the transition matrix and multidimensional Gaussian fuzzy sets yielding initial automaton states from relevant input variables and target states. Computer simulation results are presented to show learning performance of the final algorithms.

Parallel Simulation Decision-Making Method for a Response to Unconventional Public Health Emergencies Based on the Scenario-Response Paradigm and Discrete Event System Theory.

Given the non-repeatability, complexity, and unpredictability of unconventional public health emergencies, building accurate models and making effective response decisions based only on traditional prediction-response decision-making methods are difficult. To solve this problem, under the scenario-response paradigm and theories on parallel emergency management and discrete event system (DES), the parallel simulation decision-making framework (PSDF), which includes the methods of abstract modeling, simulation operation, decision-making optimization, and parallel control, is proposed for unconventional public health emergency response processes. Furthermore, with the example of the severe acute respiratory syndrome (SARS) response process, the evolutionary scenarios that include infected patients and diagnostic processes are transformed into simulation processes. Then, the validity and operability of the DES-PSDF method proposed in this paper are verified by the results of a simulation experiment. The results demonstrated that, in the case of insufficient prior knowledge, effective parallel simulation models can be constructed and improved dynamically by multi-stage parallel controlling. Public health system bottlenecks and relevant effective response solutions can also be obtained by iterative simulation and optimizing decisions. To meet the urgent requirements of emergency response, the DES-PSDF method introduces a new response decision-making concept for unconventional public health emergencies.

A hybrid offline-online approach to adaptive downlink resource allocation over LTE

Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devices. This is because an optimal resource allocation scheme which attempts to multiplex the available bandwidth in order to maximize Quality of service &#x0028 QoS &#x0029, will pose an exponential computational burden at eNodeB. In order to minimize such computational overhead, this work proposes a hybrid offline-online resource allocation strategy which effectively allocates all the available resources among flows such that their QoS requirements are satisfied. The flows are firstly classified into priority buckets based on real-time criticality factors. During the offline phase, the scheduler attempts to maintain the system load within a pre-specified safe threshold value by selecting an appropriate number of buckets. This offline selection procedure makes use of supervisory control theory of discrete event systems to synthesize an offline scheduler. Next, we have devised an online resource allocation strategy which runs on top of the offline policy and attempts to minimize the impact of the inherent variability in wireless networks. Simulation results show that the proposed scheduling framework is able to provide satisfactory QoS to all end users in most practical scenarios.

Parallel Automaton Representation of Marine Crafts’ COLREGs-based Manoeuvering Behaviours

With international rules of navigation, the IMO COLREGS, describing the regulatory behaviours of marine vessels relative to each other, correct interpretation of situations is instrumental to the successful navigation at sea. This becomes even more crucial when temporal unattended bridge or fully unmanned navigation is aimed at. Based on a breakdown of COLREG rules, this paper presents a framework for representation of manoeuvering behaviours, that are expected when all vessels obey the rules. Our analysis is based on discrete-event systems theory and the proposed framework consists of sets of finite automata, segregating situation assessment from decision making. A intermediate supervisory layer coordinates the communication of these automata modules. The framework is tested in simulation environment using a realistic scenario.

On Reconfiguration Theory of Discrete-Event Systems: From Initial Specification Until Final Deployment

This paper presents an overview on different research activities that we did in the recent decade for developing reconfigurable discrete-event systems (RDESs) from initial high-level specification according to user requirements until final low-level deployment in target hardware components. A reconfiguration is any run-time scenario that adapts the system’s behavior to any evolution in the related environment by adding or removing services or also configuring their parameters, i.e., their frequency, execution time, or also their location in the considered hardware architecture. Since the development of distributed RDESs under functional and extra-functional constraints is required by experts, we propose a complete methodology that deals first with their initial design with a new general profile named R-UML extending unified modeling language (UML) or also with new specific technology-oriented profiles, the validation of the related models with a new language R-OCL extending object constraint language (OCL), before their transformation to formal formalisms, such as Petri nets, timed automata, or B method for simulation or also formal verification of different properties. The checked models are transformed into OS reconfigurable tasks in the operational level, before applying a co-design methodology under functional, real-time, memory, and energy constraints for minimizing redundancies in tasks and for optimizing the composition of software and hardware parts together. We describe technology-oriented solutions for the scheduling of distributed RDESs by parameterizing tasks and their exchanged messages between multi-speed networked processors. We finish with the real low-level deployment on target hardware devices before applying useful software and hardware tests for checking the delivered system quality. These contributions, published in different journal and conference papers, are applied to different applications in medicine, wireless sensor networks, transportation systems, manufacturing industry, smart grids and microgrids, embedded technologies, and so on. We find significant gains in terms of the system reactivity and flexibility under related constraints.

Modeling, Simulation and Optimization for the Medical Treatment Service System based on Discrete Event System Theory

Modeling, Simulation and Optimization for the Medical Treatment Service System based on Discrete Event System Theory

A Supervisory Control Theoretic Approach to the Analysis of Democratic Progress

In this paper, we show that supervisory control theory of discrete event systems can serve as a methodology to analyze the principle of democratic progress. For this purpose, we address a new supervisory control problem in which each local supervisor is designed with its own private specification, and an additional progressive specification is given as a global objective for a system to follow. We present the controllability and power-observability as the main conditions for a controlled system to meet a progressive specification in the sense that the number of local supervisors meeting private specifications increases as the controlled system evolves. We then demonstrate that the developed theory can be used to analyze the principle of democratic progress in a systematical way through a case study of labor unions in the Unites States.

Exploiting Approximations in Supervisory Control with Distinguishers

Distinguishers have been used in Supervisory Control Theory (SCT) of Discrete Event Systems, as a way to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. Despite the advantages, supervisors obtained from approximated DES plants can be suboptimal, so that the modeling structure has to be reconstructed empirically, until the proper supervisor is found. In this paper we show how to construct suitable-approximations that always lead to least restrictive controllable synthesis. A manufacturing system example is provided to illustrate the approach.

A Learning-Based Synthesis Approach to the Supremal Nonblocking Supervisor of Discrete-Event Systems

This paper presents a novel approach to synthesize supremal nonblocking supervisors of discrete-event systems (DES), when the automaton models of specifications are not available. Extending the L* learning algorithm, an S* algorithm is developed to infer a tentatively correct supervisor. If the tentatively correct supervisor is nonblocking, it is indeed the supremal nonblocking supervisor with respect to the plant and specifications. Otherwise, the blocking automaton is regarded as a new plant, and the specification is the nonblocking property. Then, the supremal nonblocking supervisor with respect to the new problem is computed using supervisory control theory of DES. Two simplification rules are introduced to the S* algorithm to decrease the computational cost. Finally, the S* algorithm is implemented based on the LearnLib framework, and experiments are performed to verify the proposed approach.

Efficient Implementation of Distinguished Controllers for Discrete-Event Systems

In Supervisory Control Theory (SCT) of Discrete Event Systems, event refinements have been exploited to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. In spite of advantageous to modeling and synthesis, refinements do not directly cover the SCT implementation phase. In general, controllers obtained with and without refinements are expected to be implemented with equivalent hardware cost. This paper presents a decentralized architecture that extends the gains provided by refinements from synthesis to implementation. The approach separates supervisor and distinguisher codes in two different structures, which then communicate in such a way that the resulting control action on the plant is equivalent to the centralized case, while the implementation is conducted with hardware savings. An example of a buffering system illustrates the approach.

Container – Terminal – Gate – System Optimization

The utility of the container terminal has become quite remarkable all over the world. It is vital to maximize the utility of the container yard and the terminal gate system. With the ever-increasing importance of containerization in the world, more than 60% of cargo is transported by containers, this ratio even reaches 100% in some highly economic developed and political stable places. It is estimated that, the total quantity of containerization in the world will increase by 10% every year before 2020. The aim of this work is to be used as a decision support system tool when designing and constructing container terminals. The article focuses on optimizing the staging of containers within a terminal gate system. A Port within the main Land China is being used as a test bed to demonstrate the Simulation Model based on Queuing Theory. The analysis considers both the facility and the setting of the terminal gate system by considering the Discrete Event System Theory and the terminal gate system in order to make the daily operation more suitable for the terminal. The research combines the computer simulation method and the actual complex discrete event system, and identifies the most suitable setting and the theoretical maximum capacity for the terminal. Normally, the usage ratio (the busy time) should be around 75%, the simulation findings, the channel performance measures indicators are the usage ratio and the average waiting time the proper number of channel is 3 for entrance channel, and 2 for exit channel with usage ratios of 74% and exit usage of 55%, allocating 3 Entrance Channel (gates) and 2 Exit Channels (gates). Both the entrance and exit saved 2 channels. Both the capacity and channel number calculation are treated as a reference for the future decision maker when it comes to the container terminal construction.

離散事象システム理論 : 来し方と現在 (離散事象システム理論の新たな展開特集号)

離散事象システム理論 : 来し方と現在 (離散事象システム理論の新たな展開特集号)