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Actuator Fault Estimation Using Neuro-Sliding Mode Observers

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Reformulated principle for designing actuator fault estimation for continuous-time linear MIMO systems, based on neuro-sliding mode observer structure, is presented in this paper. Radial basis function neural network is used as a model-free fault approximator of the unknown additive fault. The method utilizes Lyapunov function and the steepest descent rule to guarantee the convergence of the estimation error asymptotically, where the design parameters can be obtained using LMI techniques. Finally, the proposed fault estimation scheme is applied to a nonlinear water tank system and simulation results illustrate its satisfactory performance.
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A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers. © 2008 Springer-Verlag Berlin Heidelberg. All rights are reserved.
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