New H c , controller for neural networks subject to time-varying delay by state estimation based approach

This study focuses on the H Po control issue for neural networks (NNs) subject to time-varying delay and external disturbances. The main objective is to construct anew H Po controller to ensure the asymptotical stability of the closed-loop system in the case when the neuron states are not measurable. By establishing an appropriate Lyapunov-Krasovskii functional (LKF) with single and double integral terms and utilizing some effective integral inequalities, the state estimator is presented. Then, according to the estimated state, amore effective H Po controller is further developed, and by utilizing a decoupling technique, some less conservative results are derived which guarantees that the concerned system is asymptotically stable and achieves given H Po performance. At the end, the advantages and less conservativeness of the presented method is shown by one well-known simulation example.