To solve the consensus problem of fractional-order multiagent systems with nonzero initial states, both open- and closed-loop PDα-type fractional-order iterative learning control are presented. Considering the nonzero states, an initial state learning mechanism is designed. The finite time convergences of the proposed methods are discussed in detail and strictly proved by using Lebesgue-p norm theory and fractional-order calculus. The convergence conditions of the proposed algorithms are presented. Finally, some simulations are applied to verify the effectiveness of the proposed methods.
A novel compact planar dual‐band multiple input multiple output (MIMO) antenna with four radiating elements for 5G mobile communication is proposed. Each radiating element has a planar folded monopole, which is surrounded by L‐shaped meta‐rim extended ground stubs. The compact folded arms act as the main radiating elements, while combined with the L‐shaped meta‐rim stubs, the proposed antenna forms multiple resonances so as to achieve dual‐band coverage. The simulated and measured results show that the proposed antenna has two wide bands of −6 dB return loss, consisting of 1.6 to 3.6 and 4.1 to 6.1 GHz, respectively. Without any additional isolation structure between the elements, the isolation for the proposed 2 × 2 MIMO antenna in both desired bands can be achieved better than 12 dB. The measured results show that the proposed MIMO antenna with good performance, that is, stable radiation patterns, high efficiencies, low specific absorption ratio (SAR) to human tissues, is suitable for WLAN/LTE, 4G and future 5G mobile phone applications.
In this work, the consensus problem of fractional-order multiagent systems with the general linear model of fixed topology is studied. Both distributed PDα-type and Dα-type fractional-order iterative learning control (FOILC) algorithms are proposed. Here, a virtual leader is introduced to generate the desired trajectory, fixed communication topology is considered, and only a subset of followers can access the desired trajectory. The convergence conditions are proved using graph theory, fractional calculus, and λ norm theory. The theoretical analysis shows that the output of each agent completely tracks the expected trajectory in a limited time as the iteration number increases for both PDα-type and Dα-type FOILC algorithms. Extensive numerical simulations are given to demonstrate the feasibility and effectiveness.
This article presents a novel wideband bandpass filter based on the integration of a substrate integrated waveguide (SIW) and a spoof surface plasmon polariton (SSPP). An SIW cavity with periodic arrays of meander-slot units is etched on the top metallic layer to achieve the characteristics of a multi-order filter with good performance. The passbands can be flexibly selected by varying the geometric parameters of the SIW and SSPP to adjust the lower and upper sidebands independently. Using a redistribution layer (RDL) process, a novel 3D capacitive interconnection called a through-dielectric capacitor (TDC) is proposed and collaboratively designed with an interdigital capacitor to achieve capacitive source-load cross-coupling. The proposed filter has a center frequency of 60 GHz, with a wide 3-dB fractional bandwidth of about 45.8%. The improved simulated sideband suppression has a 30 dB rejection at 40 GHz and 75.4 GHz, corresponding to a 30-dB rectangular coefficient of 1.28.
Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast.
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