Fault diagnosis for a class of discrete-time delayed complex interconnected networks with linear coupling in the case of actuator fault is studied. For the case of unavailability of network state, a state observer is first designed. Then a fault diagnosis observer is designed to detect the actuator fault on the basis of online adaptive approximator, which can approximate the unmodeled dynamics of the complex networks. Lastly, by choosing a suitable threshold, the actuator fault can be detected. A numerical simulation is used to show the effectiveness of the proposed method.
The stability analysis of Cohen-Grossberg neural networks with multiple delays is given. An approach combining the Lyapunov functional with the linear matrix inequality (LMI) is taken to obtain the sufficient conditions for the globally asymptotic stability of equilibrium point. By using the properties of matrix norm, a practical corollary is derived. All results are established without assuming the differentiability and monotonicity of activation functions. The simulation samples have proved the effectiveness of the conclusions.
This paper investigates the anti-synchronization problem of a class of novel chaotic systems with fully unknown parameters based on the adaptive control method. By virtue of the definition of anti-synchronization error signal as the sum of state signals of the drive system and the response system to be synchronized, the synchronization error system is derived. Then, a simple adaptive state feedback controller with proper parametric adaptive law is designed to stabilize the synchronization error system based on the Lyapunov stability theory. Finally, an illustrative example is presented to show the effectiveness of the proposed method.
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