Fuzzy self-organizing controller and its application for dynamic processes
Fuzzy Sets and Systems - Fuzzy Control
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
A course in fuzzy systems and control
A course in fuzzy systems and control
Fuzzy Control
Computers in Industry - Special issue: Soft computing in industrial applications
Design and Stability Analysis of Fuzzy Model-based Predictive Control—A Case Study
Journal of Intelligent and Robotic Systems
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
A fuzzy-neural multi-model for nonlinear systems identification and control
Fuzzy Sets and Systems
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
Fuzzy Sets and Systems
An advanced FMRL controller for FACTS devices to enhance dynamic performance of power systems
International Journal of Automation and Computing
Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism
Expert Systems with Applications: An International Journal
Dynamically focused fuzzy learning control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
Fuzzy learning control for a flexible-link robot
IEEE Transactions on Fuzzy Systems
Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems
Knowledge-Based Systems
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An improved adaptation mechanism to fuzzy model reference learning control (FMRLC) will be introduced in this paper. The main idea of the presented approach consists in including the controller input fuzzy sets into the adaptation process. In comparison with other FMRLC modifications the proposed method can be started with smaller number of input membership functions resulting in better reference signal tracking. Performance of the proposed procedure is demonstrated on control of a nonlinear laboratory system.