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Barrier Lyapunov function–based adaptive neural network control for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints via command filtering
Journal of Vibration and Control ( IF 2.8 ) Pub Date : 2020-09-27 , DOI: 10.1177/1077546320962639
Senkui Lu 1 , Xingcheng Wang 1
Affiliation  

This article considers the problem of adaptive neural network control via command filtering for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints and parameter uncertainties. First, a neural network state observer based on a K-filter is established to reconstruct unmeasured feedback information. Then, the command filtered technology is used to overcome the inherent “explosion of complexity” problem under fractional-order framework. Furthermore, to eliminate the errors generated by filters, an error compensation system is used. Meanwhile, the nonlinear unknown functions are approximated by using neural networks. In addition, the barrier Lyapunov functions are designed to avoid the violation of the state constraints. Finally, the availability of the proposed control algorithm is revealed by numerical simulations.



中文翻译:

基于Barrier Lyapunov函数的自适应神经网络控制,通过命令滤波对具有全状态约束的不定比例分数阶混沌永磁同​​步电动机进行控制

本文考虑了具有全状态约束和参数不确定性的不相称分数阶混沌永磁同​​步电动机通过命令滤波进行自适应神经网络控制的问题。首先,建立基于K滤波器的神经网络状态观测器,以重建未测得的反馈信息。然后,使用命令过滤技术来克服分数阶框架下固有的“复杂性爆炸”问题。此外,为了消除滤波器产生的误差,使用了误差补偿系统。同时,利用神经网络对非线性未知函数进行逼近。此外,屏障Lyapunov函数旨在避免违反状态约束。最后,

更新日期:2020-09-28
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