Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach

Rakkiyappan, R.; Zhu, Quanxin; Radhika, T.

doi:10.1007/s11071-013-0870-3

Cited by 27 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using Schur complement, it is easy to see that inequalities (32)-(35) are equivalent to the LMIs in (12). According to LMIs in (12), we have J (t) < 0 which implies that ||y(t)|| 2 < γ ||w(t)|| 2 holds for any nonzero w(t) ∈ l 2 [0, ∞].…”

Section: It Follows From Lemma 23 That the Inequality (24) Is True Imentioning

confidence: 99%

“…More precisely, discrete-time communication is used with sampled-data information instead of continuous-time communication, which is totally different from existing research works and is more useful in realistic situations [2,4,8,11,12]. By adapting the key idea from sampled-data control systems [3,17,20], in this work, only the samples of the control input signals at discrete time instants will be employed for investing the stability of flexible spacecraft system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault-tolerant sampled-data control of flexible spacecraft with probabilistic time delays

2014

View full text Add to dashboard Cite

In this paper, fault-tolerant sampled-data control for flexible spacecraft in the presence of external disturbances, partial actuator failures and probabilistic time delays is investigated. In particular, unlike the common assumptions on continuous-time information on control input, a more realistic sampled-data communication strategy is proposed with probabilistic occurrence of time-varying delays which is modeled by introducing Bernoulli distributed sequences. The main purpose of this paper is to derive fault-tolerant sampled-data control law which makes the closed-loop system robustly asymptotically stable with a prescribed upper bound of the cost function about its equilibrium point for all possible actuator failures. More precisely, by constructing an appropriate Lyapunov-Krasovskii functional involving the lower and upper bound of the probabilistic time delay, a new set of sufficient conditions are derived in terms of linear matrix inequalities for achieving the required result. Numerical simulations are presented by taking the real parameters to the considered aircraft model, which is not only highlighting the ensured closed-loop performance by the proposed control law, but also illustrates its superior fault tolerance, fast convergence and robustness in the presence of external disturbances and actuator faults when compared with the conventional controller. The simulation result reveals the effectiveness and potential of the proposed new design techniques.

show abstract

Section: It Follows From Lemma 23 That the Inequality (24) Is True Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault-tolerant sampled-data control of flexible spacecraft with probabilistic time delays

2014

View full text Add to dashboard Cite

show abstract

“…For establishing the required results, the derivative of the LKF has been estimated by utilizing the quadratic convex combination technique, which is different from the linear convex combination and inverse convex combination that are extensively used in recent literature on systems with time-varying delay (see, [27][28][29][30][31][32], respectively). An amazing feature of our methodology is that we resort to neither Jensen's inequality with the delay-dividing approach nor the Leibniz-Newton formula with the free-weighting matrix method.…”

Section: Introductionmentioning

confidence: 99%

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

Nagamani

Radhika

2015

Nonlinear Dyn

Self Cite

View full text Add to dashboard Cite

This paper studied dissipativity and passivity analysis of T-S fuzzy neural networks with distributed and probabilistic time-varying delay via quadratic convex combination approach. By introducing a stochastic variable with the Bernoulli distribution, the fuzzy neural networks with random time delays are transformed into one with deterministic delays and stochastic parameters. Moreover, it is well known that the dissipativity behavior of fuzzy neural networks is very sensitive to the time delay in the leakage term. By constructing proper Lyapunov-Krasovskii functional, new delay-dependent dissipativity and passivity conditions are derived in terms of linear matrix inequalities. Different from previous results, involving the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function. Finally, numerical examples are provided to verify the effectiveness of the presented results.

show abstract

“…Motivated by above results, [20] proposes improved stochastic stability conditions for MJLSs with interval time delay. Regarding the stability analysis of Markovian jump neural systems with mixed delay, sample data control, impulse control, and exponential stability are investigated in [21][22][23][24][25][26][27]. With resorting to new technique to deal with the time delay, based on a Lyapunov-Krasovskii functional and the stochastic analysis theory, some novel sufficient conditions are established in the framework of linear matrix inequalities [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Further Results on Stability Analysis of Discrete-Time Markov Jump Linear Systems with Time-Varying Delay and Partly Known Transition Probabilities

Zhao

Han

Zhao

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper is concerned with the stability analysis of discrete-time Markov jump linear systems (MJLSs) with time-varying delay and partly known transition probabilities. The time delay is varying between lower and upper bounds, and the partly known transition probabilities cover the cases of known, uncertain with known lower and upper bounds, and completely unknown, which is more general than the existing result. Via constructing an appropriate Lyapunov function and employing a new technique to separate Lyapunov variables from unknown transition probabilities, a novel stability criterion is obtained in the framework of linear matrix inequality. A numerical example is given to show the effectiveness of the proposed approach.

show abstract

Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach

Cited by 27 publications

References 35 publications

Fault-tolerant sampled-data control of flexible spacecraft with probabilistic time delays

Fault-tolerant sampled-data control of flexible spacecraft with probabilistic time delays

Dissipativity and passivity analysis of T–S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach

Further Results on Stability Analysis of Discrete-Time Markov Jump Linear Systems with Time-Varying Delay and Partly Known Transition Probabilities

Contact Info

Product

Resources

About