ABSTRACT
This brief presents a new guaranteed performance state estimation criterion for delayed static neural networks. To facilitate the use of the slope information about activation function, the estimation error of activation function is separated into two parts for the first time. Then, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which has fully captured the slope information of the activation. Based on the new LKF, a less conservative design criterion of estimator is derived to ensure the asymptotic stability of estimation error system with performance. The desired estimator gain matrices and the performance index are obtained by solving a convex optimization problem. The simulation results show that the proposed method has much better performance than the most recent results.
ABSTRACT
This paper is concerned with the guaranteed cost control for continuous-time singular Markovian jump systems with time-varying delay. Without using the free weighting matrices method, a delay-range-dependent condition is derived in terms of strict linear matrix inequality (LMI), which guarantees that the singular system is regular, impulse free and mean-square exponentially stable with an H(∞) performance. Based on this, the existence condition of the guaranteed cost state feedback controller is proposed. A numerical example is given to illustrate the effectiveness and less conservatism of the proposed design method.
