H∞ control design for memristor-based neural networks subject to actuator saturation
Xiaowei Zhang, Huai‐Ning Wu · 2017
This paper investigates the H∞control design for memristor-based neural networks (MNNs) in the presence of actuator saturation and external disturbance. Initially, using characteristic function technique, we transform a general model of MNNs to a new form to solve the control design problem. Then, by constructing a appropriate Lyapunov function, a constrained H∞control design is developed to exponentially stabilize the MNNs while satisfying a prescribed performance of disturbance attenuation, where the sector nonlinearity model is adopted to deal with the input saturation and the existence condition of the constrained H∞controllers is provided in terms of linear matrix inequalities (LMIs). Moreover, a region of exponential stability for the saturated MNNs is also given. Finally, an illustrative example is presented to show the feasibility and effectiveness of the proposed design method.