Jeffrey Ashley

Automated synthesis and composition of taskblocks for control of manufacturing systems

Automated control synthesis methods for discrete-event systems promise to reduce the time required to develop, debug, and modify control software. Such methods must be able to translate high-level control goals into detailed sequences of actuation and sensing signals. In this paper, we present such a technique. It relies on analysis of a system model, defined as a set of interacting components, each represented as a form of condition system Petri net. Control logic modules, called taskblocks, are synthesized from these individual models. These then interact hierarchically and sequentially to drive the system through specified control goals. The resulting controller is automatically converted to executable control code. The paper concludes with a discussion of a set of software tools developed to demonstrate the techniques on a small manufacturing system.

Qualitative Diagnosis of Condition Systems

A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system fault is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A diagnosis of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a fault.

Elaborative orderings of condition languages

Considers condition languages, where the language specifically specifies sequences of system inputs and outputs that are true, rather than just indicating the changes as events. This avoids the potential state explosion of the commonly found event-based automata, which must keep track of the current state of the system inputs through its own state. We introduce an elaborative ordering, which is used to compare the amount of detail among sequences. This allows us to clearly relate the detailed closed-loop condition language of a system to high-level, less-detailed sequences specifying desired behaviors.

Diagnosis of condition systems using causal structure

In a system composed of multiple subsystems, each subsystem imposes constraints on the behavior of the system through its interactions with other subsystems and with the external environment. A failure occurs when the subsystem no longer imposes its constraints, thus permitting unexpected observed behaviors from the system. In this paper, we consider systems composed of interacting condition systems, a form of Petri net with input and output signals defining its interactions with other subsystems. We present a method of transforming the system model into a causal model defining which subsystems can potentially affect other subsystems. When observed system outputs are not consistent with expected behaviors, then the causal model is analyzed to present a set of diagnostic hypotheses. It is shown that this set of hypotheses is a superset of the subsystems which could account for the failure.

State observability and condition observability for a class of interacting discrete event systems

This paper introduces the concepts of state observability and condition observability for condition systems, a class of systems composed of discrete state components which interact via discrete binary signals called conditions. Given a set of externally observed conditions, state observability implies that the state of the system can be determined from the observations, and condition observability implies that all unobserved input and output conditions of the system can be determined from the observations. In this paper, we present a class of systems which is state observable and condition observable. We present a method to synthesize an observer system to provide state and condition signal estimates for a single component subsystem.

Diagnosis of condition systems using diagnostic causal networks

A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system failure is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A. diagnosis of this failure localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a failure.

Characterizing uncontrollable reachability for colored controlled Petri nets

This paper establishes the groundwork for a forbidden state control synthesis method for controlled colored Petri nets (CtlCPN). In particular, this paper investigates characterizing uncontrollable reachability for CtlCPNs by developing algebraic expressions from specifications of forbidden states. These expressions represent the uncontrollable dynamics of a net model. For any net marking (state), these expressions are evaluated online to determine whether forbidden markings are reachable.<<ETX>>

Qualitative diagnosis of condition systems for multiple subsystem failures

Condition systems are a form of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In previous work, fault diagnosis was defined in terms of observed behavior versus expected behavior of subsystem models under a single fault assumption, where the expected behavior is defined through condition system models, and approximate methods were presented for detection and diagnosis. In this paper, we present an exact diagnosis method for a system that may experience multiple subsystem faults.

Exploiting causal structure in the refined diagnosis of condition systems

A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In previous work, fault diagnosis was defined in terms of observed behavior versus expected behavior of subsystem models, where the expected behavior is defined through condition system models, and approximate methods were presented for detection and diagnosis. We have also presented a method to determine a best possible diagnosis within the constraints of observability. However this method requires significant state space exploration. In this paper, we wish to exploit the causal structure imposed on the system by a partition of subsystem models in order to reduce (in certain situations) the amount of work required to perform a diagnosis.

An Equivalent CTL Formulation for Condition Sequences

A condition system is a form of Petri net that interacts with other condition systems and the environment via state-based signals called conditions. The condition language framework has been used in previous papers to characterize the input/output behavior of such interacting systems, as well as to specify desired control behavior among other things. In this paper, we show that condition sequences (the specification) and condition systems (the model of the system) have an equivalent structure in the computation tree logic (CTL) framework. The primary goals of this work are to be able to utilize existing tools for program verification for our systems, and to make our work more accessible to the temporal logic community.