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Chang-hua, Taiwan

This paper provides an improved delay-range-dependent stability criterion for linear systems with interval time-varying delays. No model transformation and no slack matrix variable are introduced. Furthermore, overly bounding for some cross term is avoided. The resulting criterion has advantages over some previous ones in that it involves fewer matrix variables but has less conservatism, which is established theoretically. Finally, two numerical examples are given to show the effectiveness of the proposed results. © 2013 ISA.Published by Elsevier Ltd. All rights reserved. Source


Liu P.-L.,Chienkuo Technical University
ISA Transactions | Year: 2012

This paper is concerned with delay-dependent robust stability for uncertain systems with time-varying delays. The proposed method employs a suitable Lyapunov-Krasovskii's functional for new augmented system. Then, based on the Lyapunov method, a delay-dependent robust criterion is devised by taking the relationship between the terms in the Leibniz-Newton formula into account. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration, and new delay-dependent sufficient stability criteria are obtained in terms of linear matrix inequalities (LMIs) which can be easily solved by various optimization algorithms. Numerical examples are included to show that the proposed method is effective and can provide less conservative results. © 2012 ISA. Source


Liu P.-L.,Chienkuo Technical University
International Journal of Innovative Computing, Information and Control | Year: 2013

In this paper, the global asymptotic stability problem is dealt with for a class of recurrent neural networks (RNNs) with time-varying delays. The time delays are not necessarily differentiable and the uncertainties are assumed to be time-varying but norm-bounded. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing the Lyapunov-Krasovskii functional and integral inequality approach, an improved delay-dependent stability criterion for delay RNNs is established in terms of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach. © 2013 ICIC International. Source


Liu P.-L.,Chienkuo Technical University
International Journal of Innovative Computing, Information and Control | Year: 2013

This paper is concerned with the stability and stabilization for singular systems with time-varying delay. Firstly, by defining a novel Lyapunov function, a delay-dependent stability criterion, which ensures that the nominal unforced singular time-varying delay system is regular, impulse free and asymptotically stable, is established in terms of integral inequality approach (IIA) and linear matrix inequalities (LMIs). Then based on the obtained criteria, the exponential robust stability and stabilization problems are solved and the explicit expressions of the desired state feedback control laws are also given. Numerical examples are given to demonstrate the effectiveness and the benefits of the obtained results. © 2013 ICIC International. Source


This paper investigates a class of delayed cellular neural networks (DCNN) with time-varying delay. Based on the Lyapunov-Krasovski functional and integral inequality approach (IIA), a uniformly asymptotic stability criterion in terms of only one simple linear matrix inequality (LMI) is addressed, which guarantees stability for such time-varying delay systems. This LMI can be easily solved by convex optimization techniques. Unlike previous methods, the upper bound of the delay derivative is taken into consideration, even if larger than or equal to 1. It is proven that results obtained are less conservative than existing ones. Four numerical examples illustrate efficacy of the proposed methods. © 2013 ISA.Published by Elsevier Ltd. All rights reserved. Source

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