Online Adaptive Fuzzy Neural Identification of a Piezoelectric Tube Actuator System

Abstract:

A coupled actuator-flap-circuit system model and its online identification are presented. The coupled system consists of a piezoelectric tube actuator, a trailing-edge flap, and a series R-L-C circuit. The properties of the coupled system are examined using a Mach-scaled rotor simulation on hovering state. According to the high nonlinear hysteretic characteristics of the coupled system, the generalized dynamic fuzzy neural networks (GD-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended ellipsoidal radial basis function (EBF) neural network is used to identify the coupled system. The structures and parameters are adaptive adjusted during the learning process, and don’t need too much expert experiences. Simulation studies show that the piezoelectric tube actuator has high authority with a broad frequency bandwidth, satisfies the requirements for helicopter vibration reduction; GD-FNN has a high learning speed, the final networks have a parsimonious network structure and generalize well, possessing broad application prospects in the helicopter vibration reduction.

• Periodical: Applied Mechanics and Materials (Volumes 275-277)
• Pages: 915-924
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.275-277.915
• Online since: January 2013
• Authors: Da Lin Yang, Wei Dong Yang, Zhu Zhang
• Keywords: Active Flap, GD-FNN, Identification, Piezoelectric Tube Actuator

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