Hardware implementation of fuzzy Petri net as a controller.
The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented.
Gniewek
Lesław
L
Department of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland.
Kluska
Jacek
J
D000465:Algorithms; D001185:Artificial Intelligence; D017076:Computer-Aided Design; D003205:Computing Methodologies; D004867:Equipment Design / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods*; D005246:Feedback; D017143:Fuzzy Logic; D016015:Logistic Models; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*