Brief Paper: Real-time control of manufacturing cells using dynamic neural networks

Authors:
George A. Rovithakis;Vassilis I. Gaganis;Stelios E. Perrakis;Manolis A. Christodoulou
Affiliations:
Department of Electronic and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece;Department of Electronic and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece;Department of Electronic and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece;Department of Electronic and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece
Venue:
Automatica (Journal of IFAC)
Year:
1999

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Abstract

In this paper, a control aspect of the non-acyclic FMS scheduling problem is considered. Based on a dynamic neural network model derived herein, an adaptive, continuous time neural network controller is constructed. The actual dispatching times are determined from the continuous control input discretization. The controller is capable of driving system production to the required demand and guaranteeing system stability and boundedness of all signals in the closed-loop system. Modeling errors and discretization effects are taken into account thus rendering the controller robust. A case study demonstrates the efficiency of the proposed technique.