In this article, the cluster synchronization problem for a class of the nonlinearly coupled delayed neural networks (NNs) in both finite- and fixed-time cases are investigated. Based on the Lyapunov stability theory and pinning control strategy, some criteria are provided to ensure the cluster synchronization of the nonlinearly coupled delayed NNs in both finite-and fixed-time aspects. Then, the settling time for stabilization that is dependent on the initial value and independent of the initial value is estimated, respectively. Finally, we illustrate the feasibility and practicality of the results via a numerical example.
No abstract is provided for this article.
No abstract is provided for this article.
ABSTRACT This article addresses the stabilization problem of networked control systems (NCSs) under non‐periodic denial‐of‐service (DoS) attacks and actuator saturation. A non‐periodic DoS attack model is put forward, taking into account the minimum communication security time period, the maximum attack duration, and the frequency constraint. This model facilitates the analysis of system performance in relation to attack characteristics by establishing connections between two generalized assumption models. For the NCSs suffering from both non‐periodic DoS attacks and actuator saturation, a dynamic memory‐based event‐triggered mechanism (DMETM) is designed to maintain control performance and reduce network resource consumption. To deal with the actuator saturation phenomenon, a set of memory‐based primary‐auxiliary controllers is designed to compensate for actuator saturation effects via convex hull representation. A guaranteed‐cost function accounting for system states and input saturation is developed. Based on the obtained switched system model and the piecewise Lyapunov‐Krasovskii functional (LKF), sufficient conditions are derived to ensure local asymptotic stability and weighted disturbance attenuation with performance. The co‐design of DMETM weighting matrices and controller gains is presented in the form of linear matrix inequalities (LMIs). Additionally, methods for estimating the attraction domain and deriving the upper bound of the cost function are given by the obtained LMIs. Finally, the effectiveness and superiority of the proposed method are validated on a high‐speed train system.
No abstract is provided for this article.
This paper is concerned with the design problem of robust switching rule for Boost converters with uncertain parameters and disturbances. Firstly, the Boost converter is modeled as a switched affine linear system with uncertain parameters and disturbances. Then, using common Lyapunov function approach and linear matrix inequality (LMI) technique, a novel switching rule is proposed such that the model reference tracking performance is satisfied. Finally, a simulation result is provided to show the validity of the proposed method.
Accepted version of an article from the journal: Nonlinear Dynamics and Systems Theory. Also available from the publisher: http://www.e-ndst.kiev.ua/v9n3.htm
In this paper by using the quantized space and time theory, the internal structure of the beauty quark has been analyzed, and the different modes for decay of beauty quark have been predicted theoretically. Also it has been explained that why the decay of beauty quark into kaon and muons is 88% of its decay into kaon, electron and positron.
We present a new design strategy that makes it possible to synthesize decentralized output-feedback controllers by solving two successive optimization problems with linear matrix inequality (LMI) constraints. In the initial LMI optimization problem, two auxiliary elements are computed: a standard state-feedback controller, which can be taken as a reference in the performance assessment, and a matrix that facilitates a proper definition of the main LMI optimization problem. Next, by solving the second optimization problem, the output-feedback controller is obtained. The proposed strategy extends recent results in static output-feedback control and can be applied to design complex passive-damping systems for vibrational control of large structures. More precisely, by taking advantages of the existing link between fully decentralized velocity-feedback controllers and passive linear dampers, advanced active feedback control strategies can be used to design complex passive-damping systems, which combine the simplicity and robustness of passive control systems with the efficiency of active feedback control. To demonstrate the effectiveness of the proposed approach, a passive-damping system for the seismic protection of a five-story building is designed with excellent results.
No abstract is provided for this article.
The manifold of a planar array in a direction finding system may be considered as two families of azimuth θ and elevation ø curves, where the ø-parameter curves are hyperhelical as well as geodesic while the θ-parameter curves are neither. Since the θ-curves are not hyperhelical, their curvatures depend on θ and so analytical evaluation of curvatures of order greater than two can become exceedingly laborious and impractical. The advantages of having hyperhelical parameter curves are numerous. For one thing, all the curvatures of a hyperhelix may be evaluated recursively (since they do not vary from point to point) as a function of lower-order curvatures. This has been demonstrated in (1) for the case of the single-parameter manifold of a linear array. Furthermore the convenient nature of a hyperhelix's geometry has proven invaluable in array design (2), in investigating the detection and resolution thresholds (3) and in identifying ambiguities inherent in array configurations (4). In view of the above facts, it seems logical that an alternative parametrization of the manifold surface, which results in two sets of hyperhelical parameter curves, can provide a great deal of additional insight into the nature of planar array behaviour and design. In this investigation, such a parametrization is identified and its significance is demonstrated by a number of examples/applications. Furthermore properties, such as Gaussian and geodesic curvatures, are defined and their implications with regards to isometric mappings are discussed.
In this paper a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system is modeled and simulated. Two well-known phenomenological hysteresis models are explored: Bouc–Wen and Dahl ones. In particular, influence of their parameters on the response is evaluated and assessed. The next step is to introduce the artificial neural networks and discuss their application in the field of systems identification. Subsequently, two feedforward neural networks are created and trained to estimate parameters characterizing each of the MR damper models described. The semi-active suspension (SAS) system equipped with a MR brake is described and the detailed procedure for acquisition of the reference data used in the models validation stage is elaborated. The models outputs obtained by simulating them with the values of coefficients as identified by the networks are compared to each other as well as to the reference experimental data. Thanks to that, the comparative analysis between the suggested vibration suppression models and the full-scale MR brake is done and it is concluded which of the discussed models has a better performance. The usability of neural networks in the field of parameters estimation of the mathematical models of the real world phenomena is described as well. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully implemented in the area of model-based control of semi-active vibration suppression systems.
This paper makes a prediction of Chinese stock index (CSI) future prices using fuzzy sets and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the research object. The fuzzy time series model combines the fuzzy theory and the time series theory, thus this model can solve the fuzzy data in stock index futures prices. This paper establishes a multivariate model and improves the accuracy of computation. By combing traditional fuzzy time series models and rough set method, we use fuzzy c-mean algorithm to make the data into discrete. Further more, we deal with the rules in mature modules of the rough set and then refine the rules using data mining algorithms. Finally, we use the CSI 300 index futures to test our model and make a prediction of the prices.
This paper investigates a convex optimization approach to the problem of robust H ∞ filtering for uncertain linear systems connected over a common digital communication network.We consider the case where quantizers are static and the parameter uncertainties are norm bounded.Firstly, we propose a new model to investigate the effect of both the output quantization levels and the network conditions.Secondly, by introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) for the existence of the desired network-based quantized filters with simultaneous consideration of network induced delays and measurement quantization.The explicit expression of the filters is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm bounded uncertainties.
