Feedback linearization using neural networks
Feedback linearization using neural networks
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DOI：
10.1109/icnn.1994.374620
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发表时间：
1994-06
期刊：
影响因子：
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通讯作者：
A. Yesildirek;F. Lewis
中科院分区：
文献类型：
--
作者：
A. Yesildirek;F. Lewis
针对一类单输入单输出（SISO）连续时间非线性系统，提出了一种基于神经网络的控制器，使系统反馈线性化。控制作用是用来实现跟踪性能的状态反馈线性化，但未知的非线性系统。在李雅普诺夫意义下给出了全局稳定性证明。结果表明，闭环系统中的所有信号和控制动作都是GUUB。无需学习阶段要求，网络的初始化非常简单。>
For a class of single-input, single-output (SISO), continuous-time nonlinear systems, a neural network-based controller is presented that feedback linearizes the system. Control action is used to achieve tracking performance for a state-feedback linearizable, but unknown nonlinear system. A global stability proof is given in the sense of Lyapunov. It is shown that all the signals in the closed-loop system and the control action are GUUB. No learning phase requirement is needed and initialisation of the network is straightforward.>