Yi-Liang Chen

Modeling and diagnosis of timed discrete event systems-a factory automation example

Detection and identification of failures is a critical task in the automatic control of large and complex systems. In the realm of discrete event systems, Sampath et al. (1995, 1996) proposed a new approach to failure diagnosis that models the logical behavior of the considered system in terms of state machines and produces an extended observer called a diagnoser for computing diagnoses. We extend this approach to the diagnosis of timed discrete event systems whose temporal and logical behavior are modeled by a framework proposed by Brandin and Wonham (1994). We use a simple real-world factory conveyor example to demonstrate our modeling and diagnosis approach.

Model-based diagnosis and control reconfiguration for discrete event systems: an integrated approach

We describe an approach to automate the dynamic computation of optimal control/reconfiguration actions that can achieve pre-specified control objectives. This approach, based on model-based diagnostic representations and algorithms, integrates diagnostics and control reconfiguration for discrete event systems using a single modeling mechanism and suite of algorithms. When the system functionality degrades (i.e., failures occur in the systems), the diagnostic algorithm will isolate the most likely failures, and then the control mechanism will generate the least-cost actions that attempt to recover from the failure and maintain the control objectives. Results about the quality of the control actions generated and the complexity of computing these actions ate also presented. We illustrate our approach using a simple wireless sensor network.

Safety control of discrete event systems using finite state machines with parameters

Control theories for discrete event systems modeled as finite state machines have been well developed over the years in addressing various fundamental control issues. However, modeling in finite state machines has long suffered the potential problem of state explosion that renders it (and, hence, the corresponding control techniques) unsuitable for many practical applications. To mitigate the state explosion problem, we proposed, in our previous work, an efficient representation that appends finite sets of parameters to finite state machines in modeling discrete event systems. We follow up in this paper to present the control synthesis techniques operating under this modeling representation. We first propose our notion and means of control under this representation. We then present our algorithms for both offline and online synthesis of safety control policies and illustrate these algorithms with examples.

Hierarchical modeling and abstraction of discrete event systems using finite state machines with parameters

A trace-based model abstraction mechanism that aggregates parameters and event sequences to a coarser, granularity is presented for discrete event systems modeled as finite state machines with parameters. Using both state transitions and parameter values for representing system behaviors and resources, the finite state machine with parameters (FSMwP) approach has resulted in efficient and compact representations for discrete event systems (in particular, those that cannot be efficiently modeled by the traditional automata-based approach). We propose a hierarchical modeling framework for FSMwPs based on an abstraction mechanism that enables automatic synthesis of models of all the entities in the hierarchy. The characteristics and advantages/disadvantages of the proposed abstraction mechanism and hierarchical framework are also discussed.

Model-based fault-tolerant control reconfiguration for general network topologies

This article describes a fault-tolerant approach to systems with arbitrary network topologies that uses a model-based diagnosis and control reconfiguration mechanism. The authors illustrate this technique using a wireless sensor network as an example.

An optimal effective controller for discrete event systems

An approach to the online synthesis of an optimal effective controller for discrete event systems is presented. The optimal effective controller can achieve the prescribed (cumulative) effectiveness measure while minimizing the total cost incurred for the execution of events. This approach is constructed over a generalized control framework for automata-based discrete event systems, which allows event enforcement in addition to the (original) event disablement/enablement as the control mechanism. The optimal effective control policy generated by this approach is proved to be the least restrictive among all the possible optimal effective control policies for the given online expansion tree of the system behavior.

Characterizing controllability and observability properties of temporal causal network modeling for discrete event systems

Control, fault monitoring, and diagnosis are critical tasks in managing discrete event systems such as real-world factory automation systems. We have applied a model-based technology based on temporal causal networks to the integrated modeling, diagnosis and reconfiguration of discrete event systems. Temporal causal networks use a propositional temporal logic with quantification over discrete time, in which the temporal sentences are constrained by the topology of the system structure that depicts the causal relations between system variables. This paper specifies for temporal causal networks some formal notions of control properties, such as observability and controllability, and the algorithmic approaches for computing these properties.

Agent-Based, Distributed Diagnosis for Shipboard Systems

The ability to reconfigure a ship’s engineering plant in response to changing mission or equipment conditions can dramatically increase a ship’s capability and survivability. We describe the agent-based, distributed diagnostic aspects of a distributed control architecture that integrates multiple ship systems and provides resource- and diagnostic-driven reconfiguration at multiple system levels, such as mission-level, process-level and component-level. We embed system- and subsystem models in distributed, agent-based components to provide distributed diagnostics. We demonstrate this architecture using a shipboard chilled water system application.

Shipboard System Diagnostics and Reconfiguration Using Model-Based Autonomous Cooperative Agents

Abstract The ability to reconfigure a ships engineering plant in response to changing mission or equipment conditions can dramatically increase a ships capability and survivability. We present a distributed control architecture for integrating multiple ship systems and providing resource- and diagnostic-driven reconfiguration at multiple system levels, such as mission-level, process-level and component-level. This architecture is being developed under a cooperative program between Rockwell and the U.S. Office of Naval Research. The architecture is based on the use of software agents distributed among networked automation controllers, which together autonomously cooperate to control and reconfigure the ships systems. System- and sub-system models are embedded in the software agents to provide diagnostics and assist in system reconfiguration when problems are detected. We also discuss the application of this architecture to a chilled water system being used as the demonstration test bed.

Model-based control reconfiguration: a shipboard system example

The ability to reconfigure a ships engineering plant in response to changing mission or equipment conditions can dramatically increase a ships capability and survivability. In our previous work (1999, 2000), a model-based reasoning framework for the integrated control/reconfiguration and diagnosis of discrete event systems was proposed. By applying this framework, we present an approach that integrates multiple shipboard systems and provides resourceand diagnostic-driven reconfiguration at multiple system levels, such as mission-level, process-level, and component-level. Several operation scenarios are studied to illustrate the reconfigurations of shipboard systems based on the changing objectives and diagnoses.