Stabilisation of singularly perturbed nonlinear systems via neural network-based control and observer design

Kuo-Jung Lin

International Journal of Systems Science, 2013, vol. 44, issue 10, 1925-1933

Abstract: This article addresses the neural network (NN)-based control and observer design for a class of singularly perturbed nonlinear (SPN) systems with guaranteed H∞ control performance. We consider the problem of NN-based observer design for SPN systems with guaranteed H∞ control performance. Based on the Lyapunov stability theorem and the tool of linear matrix inequality, we solve the controller and the observer gain matrices and some common positive-definite matrices. Then, two sufficient conditions were derived to stabilise the SPN systems. The allowable perturbation bound ε* can also be determined via some algebra inequalities, such that the proposed NN-based observer and the adaptive control will stabilise the SPN systems for all . A practical system is given to illustrate the validity of the proposed scheme.

Date: 2013
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DOI: 10.1080/00207721.2012.670304

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