The decentralized control problem for discrete-event systems addressed in this paper is that of several communicating supervisory controllers, each with different information, working in concert to exactly achieve a given legal sublanguage of the uncontrolled system's language model. A novel information structure model is presented for dealing with this class of problems. Existence results are given for the cases of when controllers do and do not anticipate future communications, and a synthesis procedure is given for the case when controllers do not anticipate communications. Several conditions for optimality of communication policies are presented, and it is shown that the synthesis procedure yields solutions, when they exist for this class of controllers, that are optimal with respect to one of these conditions.

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A sewing system is described that classifies both the fabric type and number of plies encountered during apparel assembly, so that on-line adaptation of the sewing parameters to improve stitch formation and seam quality can occur. Needle penetration forces and presser foot forces are captured and decomposed using the wavelet transform. Salient features extracted using the wavelet transform of the needle penetration forces form the input to an artificial neural network, which classifies the fabric type and number of plies being sewn. A functionally linked wavelet neural network is trained on a moderate number of stitches for five fabrics, and can correctly classify both fabric type and number of plies being sewn with 97.6% accuracy. This network is intended for use with dedicated DSP hardware to classify fabrics on-line and control sewing parameters in real time.There are many buzz words in apparel manufacturing, such as just-in-time (JIT) and quick response (QR), which reveal the desire of the apparel manufacturing industry to move away from large production runs towards smaller lots of increasingly diverse goods. It is this shift away from large runs that puts a greater demand on the efficiency of the sewing operation.At the center of the sewing operation is the sewing machine, and in general, an experienced operator is required to set up the sewing machine to properly sew each fabric type. As the manufacturing industry moves toward smaller lots with greater product variability, the sewing operation becomes increasingly inefficient due to frequent trial and error alterations of sewing machine parameters to match fabric properties. As a result, the quantity and quality of goods produced is directly related to the skill of the operator. Automation is seen as a means of deskilling the sewing operation and providing a means of achieving new methods of manufacturing such as IIT and QR.To fully automate apparel assembly, the sewing machine must be able to sense and compensate for changing sewing conditions. If the fabric type or number of plies being sewn changes, the sewing machine should detect this change and alter sewing parameters to optimize seam quality, i.e., the sewing machine should adapt.In this paper, we review several topics within the scope of on-line fabric identification [ 1 ], and present new results readily implemented to provide a powerful tool for on-line fabric classification. Fabric IdentificationThere are many means by which information can be obtained concerning fabric properties. Some of the most popular methods include Kawabata, FAST, the Hatra sewability tester, and the L&M sewability tester. Each of these testing methods has something to offer in terms of fabric evaluation. Kawabata testing (KES) is a method of fabric objective hand evaluation. Four machines are used to provide information on fabric tensile and shesring, bending, compressional, and surface pnopaties. The KES properties have been related to sewing performance of some fabrics [ 15].FAST, or fabric assurance by simpk testing. ...

A fundamental relationship between the controllability of a language with respect to another language and a set of uncontrollable events in the Supervisory Control Theory of Ramadge and Wonham and bisimulation of automata models is derived. The theoretical results relating bisimulation to controllability support an efficient solution to the Basic Supervisory Control Problem. Utilizing the bisimulation property of language controllability and derived relationships between automata languages and input/output finite-state machine behaviors, a similarity is revealed between Supervisory Control Theory and the system-theoretic problem posed by DiBenedetto et al. called Strong 1 / 0 FSM Model Matching.