@inproceedings{72d2d26648bd4a96a509b6293c82da95,
title = "H∞ control design for memristor-based neural networks subject to actuator saturation",
abstract = "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.",
keywords = "Actuator Saturation, Exponential Stability, H Control Design, Memristor-based Neural Networks",
author = "Zhang, \{Xiao Wei\} and Wu, \{Huai Ning\}",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8027984",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4000--4005",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "美国",
}