Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction
Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction
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DOI：
10.1016/j.jfranklin.2014.05.006
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发表时间：
2014-08
期刊：
影响因子：
--
通讯作者：
M. M. Arefi-M.;J. Zarei;H. Karimi
中科院分区：
文献类型：
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作者：
M. M. Arefi-M.;J. Zarei;H. Karimi
本文研究了 SISO 非仿射非线性系统的自适应输出反馈神经网络控制器设计问题。由于实际上所有系统状态在输出测量中都不可用，因此设计了观测器来估计这些状态。与现有方法相比，当前方法不需要任何有关控制增益符号的信息。为了处理控制方向的未知符号，利用了Nussbaum型函数。为了逼近未知的非线性函数，首先利用神经网络，然后使用鲁棒项来补偿逼近误差和外部干扰。该控制器基于严格正实数（SPR）李雅普诺夫稳定性理论进行设计，以确保闭环系统的渐近稳定性。最后，提出了两项仿真研究来证明所开发方案的有效性。
This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.