A Novel Adaptive Tracking Algorithm for the Resonant Frequency of EMATs in High Temperature
Keywords:
Adaptive neural network, EMATs, RBFNN, resonant frequency, transient performanceAbstract
Resonant frequency drift of electro-magnetic acoustic transducers (EMATs) is a common phenomenon in high temperature, resulting in a low conversion efficiency. In this work, for tracking the resonant frequency of EMATs automatically, a novel adaptive radial basis function neural network (RBFNN) with guaranteed transient is proposed. First, the configuration and dynamic behavior of EMATs are introduced at room temperature. Then, the initial system under the constraint of transient performance index is converted into a new unconstrained equivalent system by the error conversion mechanism. Furthermore, the adaptive tracking control for the resonant frequency is analyzed in detail. Finally, the validity and effectiveness of the proposed novel method are verified by numerical simulations.
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