控制理论(社会学)
仿射变换
饱和(图论)
自适应控制
风力发电
理论(学习稳定性)
变量(数学)
类型(生物学)
计算机科学
人工神经网络
数学
控制工程
工程类
控制(管理)
人工智能
生态学
生物
纯数学
数学分析
电气工程
机器学习
组合数学
作者
Peyman Bagheri,Laleh Behjat,Qiao Sun
标识
DOI:10.1109/iccr51572.2020.9344163
摘要
Using Nussbaum-type Functions for Multi-Input Systems with Unknown Directions, adaptive controllers are proposed and developed for a class of wind turbines with uncertainties, unknown disturbance, and subjected to input saturation. RBF neural networks are used to approximate the bounds of uncertainties and unknown disturbances sources, while Nussbaum-type Functions are employed to address the unknown input directions. Moreover, to improve the design's practicality, generic n-order dynamics are considered where there are n non-affine control inputs as opposed to widely used dynamics considering 1 non-affine input. Further, auxiliary saturation surfaces are adopted to guarantee the closed-loop system's stability in the presence of input saturation. In the end, the closed-loop system's stability is proven analytically, and simulations are rendered to visualize the analytical proof.
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