Disturbance attenuation for nonlinear switched descriptor systems based on neural network
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ORIGINAL ARTICLE
Disturbance attenuation for nonlinear switched descriptor systems based on neural network Fei Long • Yang Xiao • Xin Chen • Zhuhong Zhang
Received: 30 January 2012 / Accepted: 8 September 2012 Ó Springer-Verlag London Limited 2012
Abstract 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
F. Long (&) Y. Xiao X. Chen Z. Zhang Institute of Intelligent Information Processing, Guizhou University, Guiyang 550025, Guizhou, People’s Republic of China e-mail: [email protected] F. Long Y. Xiao X. Chen College of Computer Science and Information, Guizhou University, Guiyang 550025, Guizhou, People’s Republic of China Z. Zhang Institute of System Science and Information Technology, College of Science, Guizhou University, Guiyang 550025, Guizhou, People’s Republic of China
is given to demonstrate effectiveness of the proposed adaptive neural network-based switching control scheme. Keywords Disturbance attenuation Neural network control Nonlinear switched descriptor systems Reference model
1 Introduction As we known, many real world systems, which their dynamical behavior is not regular, may be modeled more adequately by descriptor systems. Descriptor systems, which are known as singular systems, implicit systems, generalized state-space systems or semi-state systems, are developed at 1970s. Such systems can be found in engineering systems (such as electrical circuit network, power systems, aerospace engineering and chemical processing), social systems, economic systems, biological systems and so on. Descriptor system has received much attention since this type model can preserve the structure of practical systems and can better describe a large class of physical systems than state-space ones. Although much effort had been made in the exploration of some special properties for descriptor systems, the obtained research results for such systems were mainly confined to the generalization of classical system theory. In recent years,
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