No abstract

This paper deals with the state feedback stabilization problem of delayed discrete-time Cohen-Grossberg BAM neural networks. By the mathematical induction method, stabilizable conditions are derived to ensure that the resulting closed-loop system is globally exponentially stable, and thereby, the desired state feedback controller is designed. These stabilizable conditions are very simple, which can easily verified by using the standard toolbox software (for example, MATLAB). The proposed approach is directly based on the definition of global exponential stability, and does not involve the construction of any Lyapunov-Krasovskii functional. For a special case, it is theoretical proven that the proposed method is superior to an existing one. Moreover, several illustrative examples are given to validate the success of the derived theoretical results. INDEX TERMS Discrete-time Cohen-Grossberg BAM neural network; Stabilization; Global exponential stability.

The paper is concerned with the problem of designing reliable linear-quadratic state-feedback control for a class of discrete-time singular linear systems. A reliable state-feedback control is designed for the case of possible actuator faults, and the proposed state-feedback controller guarantees that the closed-loop system is admissible and its quadratic performance index is bounded by a constant. An example is given to illustrate effectiveness of the proposed method.