A neurodynamic optimization approach to synthesis of linear systems with fault detection via robust pole assignment

This paper presents a neurodynamic optimization approach with two coupled recurrent neural networks for the synthesis of linear systems with fault detection via robust pole assignment. The proposed approach is shown to be capable of synthesizing control systems with robust state estimators and fault detection with parameter perturbation. The operating characteristics of the recurrent neural networks for state estimation and fault detection are demonstrated by using an illustrative example.