George A. Fodor

Intelligent Decision Support System

There is a need to develop an automated assessment and training procedure for children with eye-hand coordination problem. Such system is expected to reduce the burden and the associated cost of having a trained professional present at any assessment, or training session for each child. The intelligent decision support system is based upon a fuzzy automaton. By using qualitative (fuzzified) data from the previous test the system will make a decision on the complexity of the next test to be performed. A set of assessment tests, commonly used by occupational therapists, were chosen to implement the various functions using force, inertia and viscosity effects. A test bed has been designed for these tasks that consists of a six-degree-of-freedom force-reflecting haptic interface device called PHANToM along with the GHOST SDK Software, and the Intelligent Decision Support System software.

Hybrid fuzzy-Boolean automata for ontological controllers

This paper introduces a hybrid fuzzy-Boolean finite state machine (HFB FSM) model for ontological control. Ontological control is a novel type supervisory control that deals with the problems of error detection and recovery in complex control systems. The HFB FSM is used as a specification method for the problem of recovery when an autonomous control system encounters unexpected changes in its environment. The method allows a controller B (the ontological controller) to trigger a recovery operation on controller A when B has the recovery specification of A. The approach has important potential applications in industry.

Fuzzy automaton for intelligent hybrid control systems

It is a difficult problem to track the status of an event-driven, large hybrid control system. These systems often encounter unexpected events in an uncertain environment. Using a fuzzy automaton offers an effective approximation method to model continuous and discrete signals in a single theoretical framework. The concept of the virtual fuzzy automaton is used to deal with a cluster of relevant states when a decision is made on the next state of a goal path at the supervisory level. The software architecture of an autonomous agent-based industrial control system is outlined, in which the agents utilize a fuzzy automaton that is wrapped in an object.

Intelligent Decision Support System for Eye-Hand Coordination Assessment

Our goal is to develop an automated assessment and training procedure for children with eye-hand coordination problem. An automated assessment system is expected to reduce the burden and the associated cost of having a trained professional present at any assessment, or training session. The intelligent decision support system will be based upon a fuzzy automaton. By using qualitative (fuzzified) data from the previous test the system will make a decision on the complexity of the next test to be performed. A set of assessment tests, commonly used by occupational therapists, were chosen to implement the various functions using force, inertia and viscosity effects. A test bed will be used for these tasks that consists of a six-degree-of-freedom force-reflecting haptic interface device called PHANToM along with the GHOST SDK Software, and the Intelligent Decision Support System software

The virtual fuzzy state machine approach-a domain-independent fault detection and recovery method for object-based control systems

The current trend of using object based design for real time control systems has the implication that traditional state based fault detection and recovery methods cannot be used directly. This problem is even more difficult for domain independent fault detection and recovery since most such methods are based on a formal analysis of the global state set. The paper presents a domain independent fault isolation, detection and recovery method for object based control systems based on constraints of the object architecture. Variations of the control behavior from normal cases are detected using a fuzzy state machine approach.

Fuzzy logic enabled software agents for supervisory control

Programs for contemporary industrial control systems are designed using an object oriented methods and software agents. It is required that the system should reach its objectives even when unexpected events occur in an uncertain environment. A trend is that agents are becoming more autonomous, more complex and are having more responsibilities. There is a need for a high-level information representation such that a predictable behavior can be achieved in a uniform way for all agents. A fuzzy automaton-based approach offers clear benefits for developing agents of reconfigurable architecture. This paper reports on a research project that is currently underway to develop a generic encapsulated fuzzy automaton software agent for object oriented control systems. A laboratory has been set up to develop and evaluate the performance of new methods and architectures.

Using fuzzy logic for bounded recovery of autonomous agents

The solution to the problem of application-independent fault recovery of autonomous agents requires a specification method for the agents capacity to act outside of its normal operational limits. This paper presents a recovery method based upon the theory of a fuzzy finite state machine. A fuzzy specification is given for the bounds within which an autonomous agent is capable to recover after an unexpected situation has occurred in its environment. It has been shown that the three main components of the recovery problem: fault detection, fault recovery, and the properties of the actuator/sensor gear of an autonomous agent are interrelated. The suggested method can be implemented either by an application-independent software algorithm, or by fuzzy logic hardware.

Design of a reconfigurable state transition algorithm for fuzzy automata

This paper presents a reconfigurable state transition architecture for the hybrid fuzzy-Boolean finite state machine (HFB FSM). The architecture is created using parameterized components to add versatility with respect to the number of fuzzy inputs and the number of states. This is a very attractive property to implement virtual fuzzy automata for supervisory controllers of complex systems where relevant state clusters vary a great degree. The reconfigurable architecture allows the supervisory controller to reset the fuzzy automaton in order to model a particular state cluster, as needed. The design was done using VHDL.

Modeling the real-time recovery of complex control systems: a fuzzy approach

This paper presents an approach to complex system recovery based on a fuzzy specification method. The method can be applied when controllers of different types and makes are connected in a common control architecture. The method allows a controller B to trigger a recovery operation on a controller A when B has the recovery specification of A. The approach has important potential applications in industry, e.g. as a possible complement to PLC standards such as IEC1131, and to the design of hybrid and complex control systems.

Takagi-Sugeno type fuzzy automaton model

Tracking the status of an event-driven, large control system is a difficult problem. Those systems often encounter unexpected events in an uncertain environment. Using a fuzzy automaton offers an effective approximation method to model continuous and discrete signals in a single theoretical framework. A Max-Min automaton can successfully model a cluster of relevant states when a decision is to be made on the next state of a goal path at the supervisory level. However, to provide analytical proof for stability and other key properties of a fuzzy controller a Takagi-Sugeno (TS) model is preferred. In this paper a TS-type fuzzy automaton is introduced.