By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

In this brief paper, an augmented Lyapunov functional, which takes an integral term of state vector into account, is introduced. Owing to the functional, an improved delay-dependent asymptotic stability criterion for delayed neural networks (NNs) is derived in term of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative result than some existing ones. When linear fractional uncertainties appear in NNs, a new robust delay-dependent stability condition is also given. Numerical examples are given to demonstrate the applicability of the proposed approach.

In this paper, we present a robust fault-tolerant control scheme to achieve attitude control of flexible spacecraft with disturbances and actuator failures. It is shown that the control algorithms are not only attenuate exogenous bounded disturbances with attenuation level, but also able to tolerate partial loss of actuator effectiveness. The proposed controller design is simple and can guarantee the faulty closed-loop system to be quadratically stable with a prescribed upper bound of the cost function. The design algorithms are obtained by combining free weighting matrices method with linear matrix inequality technique. The effectiveness of the proposed design method is demonstrated in a spacecraft attitude control system subject to loss of actuator effectiveness.