Non-fragile sampled-data robust synchronization of uncertain delayed chaotic Lurie systems with randomly occurring controller gain fluctuation. (January 2017)

• Record Type: Journal Article
• Authors: Shi, Kaibo; Tang, Yuanyan; Liu, Xinzhi; Zhong, Shouming
• Is Part Of: ISA transactions. Volume 66(2017:Jan.)
• Page Start: 185
• Page End: 199
• Publication Date: 2017-01
• Subjects: Chaotic Lurie system -- Neural networks -- Robust synchronization -- Non-fragile sampled-data control -- Randomly occurring controller gain fluctuation; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.11.002
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper proposes a new non-fragile stochastic control method to investigate the robust sampled-data synchronization problem for uncertain chaotic Lurie systems (CLSs) with time-varying delays. The controller gain fluctuation and time-varying uncertain parameters are supposed to be random and satisfy certain Bernoulli distributed white noise sequences. Moreover, by choosing an appropriate Lyapunov-Krasovskii functional (LKF), which takes full advantage of the available information about the actual sampling pattern and the nonlinear condition, a novel synchronization criterion is developed for analyzing the corresponding synchronization error system. Furthermore, based on the most powerful free-matrix-based integral inequality (FMBII), the desired non-fragile sampled-data estimator controller is obtained in terms of the solution of linear matrix inequalities. Finally, three numerical simulation examples of Chua's circuit and neural network are provided to show the effectiveness and superiorities of the proposed theoretical results. Abstract : Highlights: Less conservative robust sampled-data synchronization conditions are obtained by developing a new non-fragile stochastic control method. By introducing two new alterable parameters and making full use of the available information about the actual sampling pattern, a novelly augmented Lyapunov-Krasovskii functional is constructed. Based on the most powerful free-matrix-based integral inequality, a desired non-fragile sampled-data estimator controller can be achieved. Numerical simulation examples of Chua's circuit and neural network are presented to demonstrate the e?ectiveness and advantages of the developed results.