This paper aims to study the fixed-time stabilization of a class of delayed discontinuous reaction-diffusion Cohen-Grossberg neural networks. Firstly, by providing some relaxed conditions containing indefinite functions and based on inequality techniques, a new fixed-time stability lemma is given, which can improve the traditional ones. Secondly, based on state-dependent switching laws, the periodic wave solution of the formulated networks is transformed into the periodic solution of ordinary differential system. By utilizing differential inclusions theory and coincidence theorem, the existence of periodic solutions is obtained. Thirdly, based on the new fixed-time stability lemma, the periodic solutions are stabilized at zero in a fixed-time, which is a new topic on reaction-diffusion networks. Moreover, the established criteria are all delay-dependent, which are less conservative than the previous delay-independent ones for ensuring the stabilization of delayed reaction-diffusion networks. Finally, two examples give numerical explanations of the proposed results and highlight the influence of delays.
In this paper, we present the design of temperature control for industrial heat treating furnace by using Intelligent Fuzzy Logic and PID controllers. Temperature control is important in heating processes as it can disqualify materials in terms of their physical properties when not well performed. Obviously PID temperature controller is the most used in industries to control non-linear processes. Consequently, it has been found that the output response from Fuzzy Logic is very accurate in terms of overshoot and steady state error when compared to that of PID. Moreover, both temperature controllers are modeled and simulated using MATLAB software.
This paper deals with the problem of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℒ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℒ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℒ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℒ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>error performance level<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math>but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.
Event-triggered communication mechanism (ETCM) provides an efficient way to reduce unwanted network traffic. This article studies the co-design of an ETCM and an annular finite-time (AFT) H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter for networked switched systems (NSSs). First, the AFT definition and ETCM are presented. Second, a set of mode-dependent average dwell-time (MADT) switching rules is given. By resorting to a delay-dependent Lyapunov functional approach, some feasible AFT H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filters are designed. Third, it is proved that the filtering error system (FES) has a good performance in attenuating the external disturbances. Finally, the feasibility of the developed method is verified via simulation.
Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.
The quality of signals plays a critical role in the diagnosis of faults in rotating machinery. It significantly impacts the accuracy of fault Identification and the precision of predicting the remaining useful life. During the signal collection process, it is inevitable that noise is acquired from the surrounding environment or other components within the rotating machinery. This noise can pose a significant challenge when attempting to extract meaningful features from vibration signals. Therefore, there is a pressing need to create a denoising model that can effectively remove noise from collected vibration signals, even in the absence of pristine, noise-free reference signals. In this study, we first developed a self-supervised denoising method for vibration signals that differs markedly from the traditional supervised learning denoising models in the field of rotating machinery, which rely on noise-clean signal pairs for training.
This paper deals with a convex optimization approach to the problem of robust network-based H ∞ control for linear systems connected over a common digital communication network with static quantizers. Both the polytopic and the norm-bounded uncertainties are taken into consideration separately. First, the effect of both the output quantization levels and the network conditions under static quantizers is investigated. Second, by introducing a descriptor technique, using a Lyapunov—Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-range-dependent linear matrix inequalities for the existence of the desired network-based quantized controllers with simultaneous consideration of network-induced delays and measurement quantization. The explicit expression of the controllers is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm-bounded uncertainties. Two examples are utilized to illustrate the design procedure proposed in this paper.
The complex dynamics that characterize hydraulic systems make it difficult for the control design to achieve prescribed goals in an efficient manner. In this paper, we present the design and analysis of a robust nonlinear controller for a nonlinear hydraulic-mechanical (NHM) system. The system consists of an electrohydraulic servo valve and two hydraulic cylinders. Specifically, by considering a part of the dynamics of the NHM system as a norm-bounded uncertainty, two adaptive controllers are developed based on the backstepping technique that ensure the tracking error signals asymptotically converge to zero despite the uncertainties in the system according to the Barbalat lemma. The resulting controllers are able to take into account the interval uncertainties in Coulomb friction parameters and in the internal leakage parameters in the cylinders. Two adaptation laws are obtained by using the Lyapunov functional method and inequality techniques. Simulation results demonstrate the performance and feasibility of the proposed method.
No abstract is provided for this article.
In this paper, the problems of estimations of faults and fault-tolerant control design are simultaneously discussed for a class of uncertain Takagi-Sugeno fuzzy descriptor systems. First, a fuzzy adaptive observer, which can decouple the external disturbances, is proposed to provide the state, actuator fault and sensor fault estimations. Then, on the basis of the estimated information, an actuator fault-tolerant controller is proposed to stabilize the descriptor closed-loop system while attenuating the disturbances by the H technique. The existence conditions of the designed observer and controller are given in terms of linear matrix inequalities, which can be solved easily by some mathematical tools. Finally, a numerical example is provided to validate the effectiveness of the proposed methods.
In this article, a computationally effective strategy to obtain multioverlapping controllers via the inclusion principle is applied to design a state-feedback multioverlapping linear-quadratic regulator controller for a 20-story building. The proposed semidecentralized controller only requires state information of neighboring stories to compute the corresponding control actions. This particular information exchange configuration allows introducing a dramatic reduction in the transmission range required for a wireless implementation of the communications system. More specifically, just a one-story transmission range is required by the proposed multioverlapping controller, while a full-building transmission range would be necessary in a classical centralized design. From a computational point of view, the presented design strategy only involves the actual computation of a reduced set of low-dimension controllers. The numerical simulations indicate that despite the simplified low-dimension design and the severe information exchange constraints, the proposed semidecentralized multioverlapping controller achieves a surprisingly high level of seismic attenuation when compared with the centralized linear-quadratic regulator controller.
