Partial Observation in Distributed Supervisory Control of Discrete-Event Systems

The signed real measure of regular languages has been introduced and validated in recent literature for quantitative analysis and synthesis of discrete-event supervisory (DES) control systems, where all events are assumed to be observable. This paper presents a modification of the language measure for supervisory control under partial observation and shows how to generalize the analysis when some of the events may not be observable at the supervisory level. Thus, for synthesis of DES control systems, the language measure of partially observable discrete-event processes is expressed in a closed form, which is structurally similar to that of completely observable discrete-event processes.

We develop a process-theoretic approach for generally-dis-tributed discrete-event systems with unrestricted nondeterminism that is geared towards supervisory control. Supervisory control theory deals with synthesis of models of supervisory controllers that ensure safe and nonblocking behavior of the supervised system. The models are synthesized based on a model of the uncontrolled system and a formalization of the control requirements. Even though there exist extensions of supervisory control theory for timed and Markovian discrete-event systems, there are hardly any investigations of supervisory control of discrete-event systems with generally-distributed delays. General distributions provide for (convenient) modeling of important real-world phenomena that cannot be consistently modeled by means of real time or Markovian (exponentially-distributed) delays, like heavy-tail or uniformly distributed processes. Our theory relies on a behavioral preorder termed partial bisimulation, for which we provide a suitable extension. Based on the proposed theory we provide for an appropriate abstraction of the stochastic behavior, which enables us to employ standard supervisory controller synthesis tools. The synthesized supervisor can, thereafter, be coupled with the stochastic model of the unsupervised system and abstracted to a generalized semi-Markov process for the purpose of analysis and simulation.

Robust nonblocking supervisory control of discrete-event systems under partial observation

Recently we proposed relative observability for supervisory control of discrete-event systems under partial observation. Relative observability is closed under set unions and hence there exists the supremal relatively observable sublanguage of a given language. In this paper we present a new characterization of relative observability, based on which we design an algorithm that computes the supremal sublanguage. This algorithm is formulated in terms of languages, thereby separating out the linguistic essence of the problem from the implementational aspects of state computation using automaton models. Exploiting the linguistic concept of support based on Nerode equivalence, we prove that for regular languages the algorithm is finitely convergent to the supremal sublanguage.

We identify a new observability concept, called relative observability, in supervisory control of discrete-event systems under partial observation. A fixed, ambient language is given, relative to which observability is tested. Relative observability is stronger than observability, but enjoys the important property that it is preserved under set union; hence there exists the supremal relatively observable sublanguage of a given language. Relative observability is weaker than normality, and thus yields, when combined with controllability, a generally larger controlled behavior; in particular, no constraint is imposed that only observable controllable events may be disabled. We design new algorithms which compute the supremal relatively observable (and controllable) sublanguage of a given language, which is generally larger than the normal counterpart. We demonstrate the new observability concept and algorithms with a Guideway and an AGV example.

In this paper, the thyristor controlled system dynamic braking resistor and the nonlinear optimal control theory are approached simultaneously within a hierarchical framework. This creates a multiple local feedback controllers that can be realistically implemented using only local measurements and whose performance is consistent with respect to changes in network configuration, loading and power transfer conditions. Following a major disturbance, the rotor angle and rotor speed of each generator unit are determined and the firing angle of the thyristor switch associated with the braking resistor is calculated by the local controllers. By controlling the firing-angle of the thyristor, braking resistor controls the accelerating power in each generator and thus enhances the stability margins and damping oscillations. Since the local controllers rely only on information particular to their own subsystem, interconnection effects and the nonlinearities introduced by them, are accounted for by a supervisory controller. The proposed control strategy was tested on the IEEE Western States Coordinating Council (WSCC) test system. Results show that the method is capable of bringing the system under control when starting with inherently unstable conditions, even when the severity of the disturbances is increased.

We consider a multi-adversary version of the supervisory control problem for discrete-event systems (DES), in which an adversary corrupts the observations available to the supervisor. The supervisor’s goal is to enforce a specific language in spite of the opponent’s actions and without knowing which adversary it is playing against. This problem is motivated by applications to computer security in which a cyber defense system must make decisions based on reports from sensors that may have been tampered with by an attacker. We start by showing that the problem has a solution if and only if the desired language is controllable (in the DES classical sense) and observable in a (novel) sense that takes the adversaries into account. For the particular case of attacks that insert symbols into or remove symbols from the sequence of sensor outputs, we show that testing the existence of a supervisor and building the supervisor can be done using tools developed for the classical DES supervisory control problem, by considering a family of automata with modified output maps, but without expanding the size of the state space and without incurring on exponential complexity on the number of attacks considered.

This paper proposes a solution to the electric Vehicle Scheduling Problem (eVSP) based on the framework of Discrete Event System (DES). A supervisory control is synthesized using the theory of Supervisory Control of Discrete Event System (SCDES) to manage the power admission control of a group of local Plug-in Electric Vehicles (PEV). The PEVs are categorized based on their level of priority to be charged and an algorithm is developed to allocate power to PEVs according to their priority and available capacity. The proposed algorithm and supervisor validates the schedulability of charging of PEVs while meeting the constraint of the grid to achieve peak demand reduction. Simulation studies are carried out using MATLAB/Simulink and STATEFLOW toolkit and the result validates the effectiveness of the control scheme to reduce peak demand while providing fair charging schedules to PEVs.

In the context of supervisory control of discrete-event systems, decentralized control is a well-established area of research. In this paper we present an implementation of such controllers for an assembly cell with two-assembly stations (SCARA robots), in a laboratory scale computer integrated manufacturing (CIM) system. Step by step implementation, conflict avoidance between local supervisors and difficulties encountered are discussed. Petri nets are used as the modelling tool and Lin and Wonham framework is used as the control architecture. Assembly stations are modelled as intelligent agents. A separate software module called order processing module (OPM) is used to handle orders into and out of the system and to schedule the two-assembly stations

In this paper we develop a data-driven approach for supervisory control of discrete-event systems (DES). We consider a setup in which models of DES to be controlled are unknown, but a set of data concerning the behaviors of DES is available. We propose a new concept of data-informativity, which captures the notion that the available data set contains sufficient information such that a valid supervisor may be constructed for a family of DES models that all can generate the data set. We then characterize data-informativity with a necessary and sufficient condition, based on which we design an algorithm for its verification.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents supervisory control of a dynamic flow controller (DFC) based on the discrete-event systems (DES) theory. A DFC can be considered as a flexible ac transmission system controller and includes a mechanically-switched phase-shifting transformer, a multimodule thyristor-switched capacitor, a multimodule thyristor-switched reactor, and a mechanically switched capacitor. Owing to the inherent discrete switching nature of a DFC, its components are modeled as finite automata; then, a DES supervisory control is designed to implement the control logic of the DFC system in different modes of operation (i.e., “automatic” and “auto/manual”). It is shown that the specifications are controllable and the synthesized supervisors are nonblocking in both modes and the modular supervisors nonconflict in auto/manual mode. </para>

Human performance in problem solving in the context of supervisory control of discrete-event systems is discussed. Software packages for such problems usually offer only rudimentary support in the form of visual data entry and algorithmic computations. Other activities such as system modeling and verification of the solution have to be done without computer support. This work is a first step in understanding how humans approach discrete-event control problems. An observational study is described, where subjects are video-taped solving supervisory control problems. One video recording is used to construct a taxonomy of the task and then the different activities of the subject are encoded accordingly. The data are analysed to discover patterns of human activity. These results can be used to guide the design of software for computer-aided problem solving in this context.

The aim of this essay is to provide a brief introduction to supervisory control theory of discrete-event systems.

Supervisory control of discrete-event systems: A brief history

As railway platforms expand in size, complexity, and traffic volume, ensuring efficiency and safety of train entrance and exit a station becomes increasingly critical. This study presents a scheduling approach for monorail one-way stations that minimises reliance on the train timetable while validating scheduling decisions against it, thereby enhancing the feasibility and safety of the scheduling process. For multi-platform stations, where switches are organised into partial binary trees, this paper introduces a modular discrete event system (DES) modelling approach to design and implement train entrance and exit schedules. The fundamental modelling unit of a multi-platform station consists of a single module, comprising an entrance/exit switch and its associated sensors. The complete DES model of the train station is then constructed as the synchronous product of multiple such modular units. In addressing train scheduling, the proposed approach incorporates fair train entrance/exit specifications, platform capacity constraints, and crossover capacity limitations. Through the supervisory control of DES, an optimal supervisor is synthesised to prevent collision incidents. This innovative methodology and accompanying algorithm facilitate seamless and equitable scheduling for train entrances and exits, without distinguishing between individual trains traversing the station.