Neuro-fuzzy Model-based Control
Journal of Intelligent and Robotic Systems
Adaptive fuzzy control systems with dynamic structure
International Journal of Systems Science
Adaptive fuzzy logic controller for DC-DC converters
Expert Systems with Applications: An International Journal
Neuro based model reference adaptive control of a conical tank level process
Control and Intelligent Systems
International Journal of Intelligent Systems Technologies and Applications
Path following and obstacle avoidance fuzzy controller for mobile indoor robots
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
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The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum