DocumentCode
2057873
Title
Dynamic scheduling approach to group control of elevator systems with learning ability
Author
Ho, Yuan- Wei ; Fu, Li-Chen
Author_Institution
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume
3
fYear
2000
fDate
2000
Firstpage
2410
Abstract
A hybrid model of a multiple elevator system is proposed, consisting of a color-timed transition Petri net (CTTPN) model and a set of control rules implemented via the so-called control places in the CTTPN model. The Petri net model is a highly modular structure, whose constituent modules can be classified into four types: call management module, loading/unloading module, basic movement module, and direction reversing module. The whole complete model is a combination of the copies of the above four modules. Since the firing sequences of the CTTPN equate the evolution of the modeled system, they can be regarded as a schedule. A dynamic scheduling with learning ability is proposed to obtain the desirable schedule. A new concept of control places is also introduced in the proposed model so as to make the modeling more precise and to reduce the reachability graph more efficiently. To show the feasibility of the proposed method, an emulator in elevator control kernel and elevator scheduler kernel were constructed for demonstration
Keywords
Petri nets; controllability; learning systems; lifts; neurocontrollers; scheduling; call management module; color-timed transition Petri net; direction reversing module; dynamic scheduling; elevator systems; group control; learning system; loading module; movement module; neurocontrol; reachability graph; Application software; Artificial intelligence; Control systems; Dynamic scheduling; Elevators; Integrated circuit technology; Job shop scheduling; Kernel; Optimal scheduling; Power system modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation, 2000. Proceedings. ICRA '00. IEEE International Conference on
Conference_Location
San Francisco, CA
ISSN
1050-4729
Print_ISBN
0-7803-5886-4
Type
conf
DOI
10.1109/ROBOT.2000.846388
Filename
846388
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