Recurrent-fuzzy-neural system-based adaptive controller for nonlinear uncertain systems

Tzu Wei Hu, Ching Hung Lee

研究成果: Conference article同行評審


This paper aims to treat the adaptive control of nonlinear system with un-model uncertainty and bounded disturbance by using a novel recurrent fuzzy neural system. The used recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A) combines the interval asymmetric type-2 fuzzy sets and fuzzy logic system and implements in a five-layer neural network structure which contains four layer forward network and a feedback layer. The RT2FNN-A is modified to provide memory elements for capturing the system's dynamic information and has the properties of high approximation accuracy and small network structure (fewer rules and tuning parameters) from the simulation results. Based on the Lyapunov theorem, adaptive updat laws of RT2FNN-A derived and the stability of closed-loop system is guaranteed for control of the nonlinear uncertain systems. Simulation result is also introduced to show the performance and effectiveness of RT2FNN-A system.

頁(從 - 到)7-12
期刊Lecture Notes in Engineering and Computer Science
出版狀態Published - 3月 2014
事件International MultiConference of Engineers and Computer Scientists, IMECS 2014 - Kowloon, Hong Kong
持續時間: 12 3月 201414 3月 2014


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