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The 1 × 2 superlative MIMO antenna structure is proposed for multiband and broadband communication applications. The presented antenna is composed of two symmetrical circular-shaped patch elements. The circular-shaped Tooth is inserted in the outer periphery of the patch area. The Radius of the patch and Tooth varies to identify the behaviour of reflectance response, directivity, and total gain. The tooth-loaded circular patch attained a better gain of 8.44 dB, broader directivity of 163°, reflectance coefficients of −29.16 dB, and good isolation among transmittance and reflectance response. The comparison between simulated and measured results is included in the analysis. Performance of the presented design structure with previously published articles to identify performance improvement. Standard performance measurement parameters like TARC, ECC, and DG are also carried out. The low-profile material FR4 is used in the proposed design as a substrate. The presented structure is suitable for fixed satellite service and Land mobile navigation applications. [ABSTRACT FROM AUTHOR]

This paper focuses on studying theH∞state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov–Krasovskii functional are handled by the Jensen’s inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable withH∞performance. The proposed conditions are represented by linear matrix inequalities. OptimalH∞norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples. [ABSTRACT FROM PUBLISHER]

This paper is concerned with $$H_\infty $$ state estimation problem of stochastic neural networks with discrete interval and distributed time-varying delays. The time-varying delay is need to be bounded and continuous. By constructing a suitable Lyapunov-Krasovskii functional with triple integral terms and linear matrix inequality technique, the delay-dependent criteria are conferred so that the error system is stochastically asymptotically mean-square stable with $$H_\infty $$ performance. The desired estimator gain matrix can be characterized in terms of the solution to linear matrix inequalities, which can be easily solved by some standard numerical algorithms. Numerical simulations are given to demonstrate the effectiveness of the proposed method. The results are also compared with existing methods. [ABSTRACT FROM AUTHOR]

In this article, we study the problem of robust H∞ performance analysis for a class of uncertain Markovian jump systems with mixed overlapping delays. Our aim is to present a new delay-dependent approach such that the resulting closed-loop system is stochastically stable and satisfies a prescribed H∞ performance level χ. The jumping parameters are modeled as a continuous-time, finite-state Markov chain. By constructing new Lyapunov-Krasovskii functionals, some novel sufficient conditions are derived to guarantee the stochastic stability of the equilibrium point in the mean-square. Numerical examples show that the obtained results in this article is less conservative and more effective. The results are also compared with the existing results to show its conservativeness. © 2016 Wiley Periodicals, Inc. Complexity 21: 460-477, 2016 [ABSTRACT FROM AUTHOR]

The main control objectives associated with the variable speed wind turbine is to extract maximum power at below rated wind speed (region 2) and to regulate the power at above rated wind speed (region 3). This paper proposes a nonlinear framework to achieve the above two control objectives. The paper discusses about the application of an integral sliding mode control (ISMC) in region 2 and a fuzzy based proportional integral (PI) control in region 3. Same ISMC is adopted for the stable switching between operating regions (transition region 2.5) and the control input maintains the continuity at the instant of switching. Lyapunov stability criterion is used to prove the stability of ISMC. The controllers are tested for different wind speed profiles with different turbulence component. Finally the performances of the proposed controllers are tested with nonlinear Fatigue, Aerodynamics, Structures, and Turbulence WT model and the results are compared with the existing baseline + PI controllers. [ABSTRACT FROM AUTHOR]

This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed. [ABSTRACT FROM PUBLISHER]

This paper focuses on studying the H ∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov–Krasovskii functional. By constructing a suitable augmented Lyapunov–Krasovskii functional with triple integral terms and linear matrix inequality technique, the delay-dependent criteria are conferred so that the error system is globally asymptotically stable with H ∞ performance. The activation functions are assumed to satisfy sector-like nonlinearities. The desired estimator gain matrix can be characterized in terms of the solution to linear matrix inequalities, which can be easily solved by some standard numerical algorithms. Numerical simulation is given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results. [ABSTRACT FROM AUTHOR]