Fuzzy self-organizing controller and its application for dynamic processes
Fuzzy Sets and Systems - Fuzzy Control
A self-tuning fuzzy controller
Fuzzy Sets and Systems
A self-organizing adaptive fuzzy controller
Fuzzy Sets and Systems
A PD-like self-tuning fuzzy controller without steady-state error
Fuzzy Sets and Systems
A modified self-organizing controller for real-time process control applications
Fuzzy Sets and Systems
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Self-organizing fuzzy control for motor-toggle servomechanism via sliding-mode technique
Fuzzy Sets and Systems - Modeling and control
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design and implementation of a fuzzy elevator group control system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
A robust self-tuning scheme for PI- and PD-type fuzzy controllers
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Improving expert meta-schedulers for grid computing through weighted rules evolution
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Engineering Applications of Artificial Intelligence
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The search for an intelligent group controller that can satisfy multi-criteria requirements of an elevator group control system has become a great challenge for researchers. This paper presents the development of an elevator group controller based on fuzzy logic framework with a self-tuning scheme. Instead of basing on predicted traffic patterns to initiate modifications in the control outputs produced, the proposed group controller utilizes average waiting time (AWT) as the measured performance criterion used to adjust the membership functions and to select appropriate fuzzy rule sets, for the generation of suitable control actions. By comparing the measured performance results with the ones desired, better adjustment of the controller can be achieved to further improve the controller's performance. Computer simulation was carried out for three different cases in three traffic peaks. The results showed considerable overall improvements in the performance criteria evaluated as compared to the performance of conventional group controllers.