Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach

Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach
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高阶不确定非仿射系统的自适应神经控制:仿射系统方法的转换

DOI:
10.1016/j.automatica.2014.03.013
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发表时间:
2014-05
期刊:
影响因子:
6.4
通讯作者:
Sun, Youxian
Sun, Youxian
中科院分区:
计算机科学2区
文献类型:
--
作者:
Meng, Wenchao;Yang, Qinmin;Jagannathan, Sarangapani;Sun, Youxian

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研究了一类动态完全未知的高阶非仿射非线性系统的自适应神经网络控制问题。由于控制项包含在未知的非线性项中,传统的控制方案和稳定性分析往往变得非常复杂。我们的主要贡献包括一个新的系统转换,通过低通滤波器和状态变换的组合将非仿射系统转换为仿射系统。因此,非仿射系统的状态反馈控制可以看作仿射系统的输出反馈控制。变换后的系统相对于新的输入成为线性的,而传统的反推方法是不需要的,从而允许合成是非常简化的。从理论上证明了闭环系统中的所有信号是一致最终有界的。仿真结果证明了所开发的控制器的性能。
This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller.
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