Disturbance attenuation for nonlinear switched descriptor systems based on neural network

作者：Long, Fei^*; Xiao, Yang; Chen, Xin; Zhang, Zhuhong

来源：Neural Computing & Applications, 2013, 23(7-8): 2211-2219.

DOI：10.1007/s00521-012-1171-3

• Disturbance attenuation
• Neural network control
• Nonlinear switched descriptor systems
• Reference model

In this paper, we address the problem of neural network-based disturbance attenuation for a class of nonlinear switched descriptor systems. An adaptive neural switching control scheme is designed so that such system can asymptotically track the desired reference model and attenuate the external disturbance to a prescribed level. By approximating the unknown nonlinear function vectors based on RBF neural network, we incorporate the network reconstruction error and exterior disturbance into the design framework of adaptive neural switching control strategy. The RBF neural network is used to compensate for the nonlinear uncertainties of switched descriptor systems, and the reconstruction error of RBF neural network is introduced to the adaptive law in order to improve the tracking attenuation quality of overall switched systems. Under the assumptions of regular and impulse free, the designed adaptive neural switching controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched descriptor system. Finally, an example is given to demonstrate effectiveness of the proposed adaptive neural network-based switching control scheme.

• 出版日期2013-12
• 单位贵州大学