A neural network-based simultaneous state and actuator fault estimation under unknown input decoupling

Abstract

The paper presents the problem of a neural network-based robust state and actuator fault estimator design for non-linear discrete-time systems. At the beginning a review of recent developments in the area of robust estimators and observers for non-linear discrete-time systems is portrayed and a less restrictive procedure for designing a neural network-based H ∞ observer is proposed. The developed approach guaranties a predefined disturbance attenuation level and convergence of the observer, as well as unknown input decoupling, and state and actuator fault estimation. The main advantage of the design procedure is its simplicity. The paper describes an observer design procedure which comes to solving a set of linear matrix inequalities. The final part of the paper shows an illustrative example concerning an application of the proposed approach to a multi-tank system benchmark.