Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A rule self-regulating fuzzy controller
Fuzzy Sets and Systems
A performance approach to fuzzy control design for nonlinear systems
Fuzzy Sets and Systems
Design of a fuzzy controller with fuzzy sliding surface
Fuzzy Sets and Systems - Special issue on fuzzy neural control
A course in fuzzy systems and control
A course in fuzzy systems and control
Design of a single-input fuzzy logic controller and its properties
Fuzzy Sets and Systems
Adaptive Control
Engineering Applications of Artificial Intelligence
Some key issues in the design of self-organizing fuzzy control systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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The dynamic response of a self-organizing fuzzy sliding-mode-controlled toggle mechanism, which is driven by a permanent magnet synchronous servo motor, is studied in this paper. First, based on the principle of fuzzy control, a self-organizing fuzzy control (SOFC) system is developed to control the position of a slider of the motor-toggle servomechanism. Moreover, to reduce the control rules in the design of the SOFC system and to strengthen the robust characteristics, a self-organizing fuzzy sliding-mode control (SOFSMC) is proposed to control the motor-toggle servomechanism. The proposed SOFC and SOFSMC systems contain two sets of fuzzy inference logic: one is the fuzzy controller and the other is the rule modifier. A new fuzzy learning method of the rule modifier is developed, where the modification value of each rule is based on the fuzzy firing weight. The proposed SOFC and SOFSMC systems can automatically tune the rules bases to achieve satisfactory performance, so that they can be applied for on-line learning, real-time control. In addition, simulated and experimental results due to periodic step and sinusoidal commands show the dynamic behaviors of the proposed SOFC and SOFSMC systems are robust with regard to parameter variations and external disturbances. Comparison between SOFC and SOFSMC also shows that the SOFSMC can reduce implementation complex while defining the sliding surface.