Compensatory fuzzy neural network control with dynamic parameters estimation for linear voice coil actuator

Chen, Syuan‐Yi; Lee, Cheng Yan

doi:10.1016/j.precisioneng.2016.12.002

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…The integration of fuzzy neural network system has catch attentions for its combination of advantages of fuzzy control and neural network [21]- [27]. An adaptive fuzzy neural network control is designed for a constrained robot using impedance learning in [21] An compensatory fuzzy neural network control is derived with dynamic parameters estimation for a linear voice coil actuator [22]. Adaptive intelligent control methods such as fuzzy control and neural control have been investigated to improve the power dynamic performance of shunt active power filter in [23]- [27].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Fuzzy Neural Controller Based on Fuzzy Sliding Mode of Active Power Filter

2020

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An adaptive backstepping fuzzy neural network (FNN) controller using a fuzzy sliding mode controller is designed to suppress the harmonics and improve the performance of a shunt active power filter (APF). A backstepping method transforms the APF system into a series of subsystems and uses the virtual control to simplify the controller design. A FNN controller is utilized to approach the nonlinear APF system. An adaptive fuzzy system is employed to adjust the sliding gain to compensate the approximation error of neural network and diminish the chattering. The weights of the fuzzy neural network and fuzzy system parameters are adjusted in real-time to guarantee the asymptotic stability of the designed control system. Simulation and experiemental studies indicate the validitiy of the proposed control method, demonstrating the good compensation performance and strong robustness. INDEX TERMS Fuzzy neural network, adaptive fuzzy control, backstepping control, active power filter.

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