Control Of Discrete Event Dynamic Systems Research Articles

A Solution of DEDS Control Synthesis Problems

A new view on the control synthesis of DEDS (discrete event dynamic systems) is presented in this paper. Petri nets (PN), frequently used for DEDS modelling, are understood here in a form of corresponding oriented graphs (OG) where the nodes of the OG are represented by the PN positions and the OG edges involve the PN transitions. The adjacency matrix of such a graph (or better said its transpose) is utilized in the DEDS control synthesis procedure. It is used for generating the numerical form of the state reachability tree in both the case of the straight-lined system development (from a given initial state to a prescribed terminal one) and that of the backtracking one (from the prescribed terminal state to the initial one). A suitable combination of both the straight-lined development of the OG-based model and the backtracking one is used in order to perform the DEDS control synthesis. Namely, the coincidence both of the reachability trees yields possible trajectories of the system development. Corresponding sequences of the control discrete events are found just by means of the OG adjacency matrix.

Automatic Solving the Problem of Control Synthesis for Manufacturing Systems

A new view on manufacturing systems (MS) modelling and control synthesis is presented. MS are understood to be a kind of discrete event dynamic systems (DEDS). Petri nets (PN), frequently used for DEDS modelling, are replaced by corresponding oriented graphs (OG). The OG nodes are represented by the PN places and the OG edges involve the PN transitions. The adjacency matrix of such a graph is utilized in the DEDS control synthesis procedure. It makes possible to generate both the straight-lined systenl development (from an initial state to a prescribed terminal one) and the backtracking one (from the terminal state to the initial one). The combination both of kinds of the model development is used to find the sequence of the DEDS control vectors. Rule-based knowledge about the control task specification can be utilized in order to choose unique (the most suitable) solution.

On-line tracing supervisory control of discrete-event dynamic systems based on outlooking

The conventional off-line approach to supervisor synthesis for discrete-event dynamic systems contains critical drawbacks in building overall automata models and calculating the complete control policy for these models. To resolve the complexity of this off-line approach, the supervisory control scheme based on limited lookahead control policies was proposed by Chung et al. and improved by Kumar et al. recently. However, these methods do not guarantee the same closed-loop behavior as an optimal off-line solution. Therefore, we propose an on-line tracing supervisory control scheme founded on component discrete-event models, which ensures the same closed-loop language as an optimal off-line scheme.

Fuzzy control in a single-machine system

Abstract As a new application area we have been tried to apply the fuzzy control concept in discrete event dynamic systems (DEDS) control problems. The fuzzy control of a single-machine system is presented. Simulation studies have been performed on the basis of a descriptive model for investigating the control performance. Two other control approaches, namely proportional control (P-control) and load-oriented order release control (LOOR-control), are employed to compare with the fuzzy control approach. It is concluded that fuzzy control is a practical and effective way to control DEDS. The fuzzy control module developed during this study can be easily adapted for fuzzy control of other DEDS, for example job-shop systems. This paper should be considered as the first step in using fuzzy control concept for the control of DEDS.

Neural Control of Discrete Event Systems

In the exotic algebrae (max,+), (min,+) the Discrete Event Dynamic Systems (DEDS) can be represented by input-output models or state space models. The continuous systems theory concepts can be utilised also for DEDS in the exotic algebrae. The paper deals with the Internal Model Control (IMC) structures for DEDS control and with the possibility to enhance the performances of this structure utilising Artificial Neural Network (ANN) for modelling and control. The behaviour of the IMC structure utilising ANN is analysed considering a simple manufacturing system represented by a timed event graph.

Aggregation and multi-level control in discrete event dynamic systems

In this paper we consider the problem of higher-level aggregate modeling and control of discrete-event dynamic systems (DEDS) modeled as finite state automata in which some events are controllable, some are observed, and some represent events to be tracked. The higher-level models considered correspond to associating specified sequences of events in the original system to single macroscopic events in the higher-level model. We also consider the problem of designing a compensator that can be used to restrict microscopic behavior so that the system will only produce strings of these primitive sequences or tasks . With this lower level control in place we can construct higher-level models which typically have many fewer states and events than the original system. The use of these higher-level models allow us to decrease the computational complexity of problems involving complex interconnected DEDS.

Applications of a Regulator Theory for Discrete-Event Dynamic Systems

As the work on the control of Discrete Event Dynamic Systems (DEDS) matures, the need for concrete applications of DEDS theory, that could direct our research efforts and emphasize the importance of the work in this field, becomes more clear. In fact, the consensus at recent DEDS sessions of various conferences has been the need for such applications. This paper introduces two applications, one on the verification and synthesis of sequential circuits and the other on the control of flexible manufacturing systems, illustrating our work on the multi-level control of DEDS. The flexible manufacturing system example is a model of the floor of of a board manufacturing plant which consists of multiple workstations. The set of tasks expected to be performed by each workstation are characterized by sets of strings of events, and the coordination of the workstations through these sets of tasks is addressed. The sequential circuit example consists of a two-port memory system which is controlled by multiple state machines which communicate through shared events. Desired memory operations are analyzed and a controller is constructed using a multilevel approach.

On the control of discrete-event dynamical systems

We study a class of problems related to the supervisory control of a discrete-event system (DES), as formulated by Ramadge and Wonham, and we focus on the computational effort required for their solution. While the problem of supervisory control of a perfectly observed DES may be easily solved by dynamic programming, the problem becomes intractable (in the sense of complexity theory) when imperfectly observed systems are considered.