Observer-based [formula omitted] control of memristor-based neural networks with unbounded time-varying delays

Abstract

This work is devoted to developing observer-based H∞ control of memristor-based neural networks with unbounded time-varying delays. A suitable observer is first designed, and then the controller is implemented based on the estimated states. Taking into account the dynamic equation of the MNN and that of the observer error, an augmented closed-loop system is given. By proposing a system solutions-based estimation method, sufficient conditions are obtained to guarantee that the augmented system is globally exponentially stable and satisfies a prescribed H∞ performance level. This approach requires neither model transformation nor the construction of Lyapunov–Krasovskii functionals. In addition, the obtained sufficient conditions contain only a few scalar inequalities, which can be easily addressed by MATLAB. Finally, illustrative simulations are given to test the validity of the theoretical results.