【摘 要】
：
提出了一种新的非线性观察器设计方法。与一般方法采用神经网络逼近整个非线性系统不同,该方法用RBF神经网络逼近系统的非线性项,故提高了状态估计的精度。基于李亚普诺夫方
【机 构】
：
南京航空航天大学自动化学院,Electrical & Computer Engineering Department University of Louisiana at Lafayette Laf
【出 处】
：
Transactions of Nanjing University of Aeronautics & Astronau
论文部分内容阅读
提出了一种新的非线性观察器设计方法。与一般方法采用神经网络逼近整个非线性系统不同,该方法用RBF神经网络逼近系统的非线性项,故提高了状态估计的精度。基于李亚普诺夫方法,证明了状态估计误差渐近稳定且渐近收敛到零。仿真结果表明,所提出的非线性观察器设计方法具有良好的性能,在故障检测、状态估计等领域具有广泛的应用前景。
A new nonlinear observer design method is proposed. Compared with the general method of using neural networks to approximate the entire nonlinear system, this method approximates the nonlinear term of the system by RBF neural network, so the accuracy of state estimation is improved. Based on the Lyapunov method, it is proved that the state estimation error is asymptotically stable and asymptotically converges to zero. The simulation results show that the proposed nonlinear observer design method has good performance and has a wide range of applications in the field of fault detection and state estimation.
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