期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 10, 页码 3027-3039出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2711496
关键词
Actuator saturation; exponential stabilization; memristive neural networks (MNNs); sampled-data control
类别
资金
- National Natural Science Foundation of China [61473070, 61433004, 61627809]
- Fundamental Research Funds for the Central Universities [N150406003]
- SAPI Fundamental Research Funds [2013ZCX01, 2013ZCX14]
This paper is concerned with the exponential stabilization of memristive neural networks (MNNs) by taking into account the sampled-data control and actuator saturation. On the one hand, the MNNs are converted into a tractable model by defining a class of logical switched functions. Based on this model, the connection weights of MNNs are dealt with by a robust analysis method. On the other hand, a saturating sampled-data controller containing an exponentially decaying term is designed. With the help of generalized sector condition and the Lyapunov stability theory, a novel sufficient condition ensuring the local exponential stability of the closed-loop systems is formulated in terms of linear matrix inequalities. In addition, three optimization problems are given to design the control gain with the aims of enlarging the sampling interval, expanding the estimation of the domain of attraction, and minimizing the size of actuators, while preserving the stability of the closed-loop systems. Two numerical examples are provided to illustrate the effectiveness of the obtained theoretical results.
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