Delay-dependent robust stabilization for uncertain singular systems with discrete and distributed delays

This paper investigates the problem of delay-dependent robust stabilization for uncertain singular systems with discrete and distributed delays in terms of linear matrix inequality （LMI） approach. Based on a delay-dependent stability condition for the nominal system, a state feedback controller is designed, which guarantees the resultant closed- loop system to be robustly stable. An explicit expression for the desired controller is also given by solving a set of matrix inequalities. Some numerical examples are provided to illustrate the less conservativeness of the proposed methods.

Self‑supervised zero‑shot dehazing network based on dark channel prior

Most learning-based methods previously used in image dehazing employ a supervised learning strategy,which is timeconsuming and requires a large-scale dataset.However,large-scale datasets are difcult to obtain.Here,we propose a selfsupervised zero-shot dehazing network(SZDNet)based on dark channel prior,which uses a hazy image generated from the output dehazed image as a pseudo-label to supervise the optimization process of the network.Additionally,we use a novel multichannel quad-tree algorithm to estimate atmospheric light values,which is more accurate than previous methods.Furthermore,the sum of the cosine distance and the mean squared error between the pseudo-label and the input image is applied as a loss function to enhance the quality of the dehazed image.The most signifcant advantage of the SZDNet is that it does not require a large dataset for training before performing the dehazing task.Extensive testing shows promising performances of the proposed method in both qualitative and quantitative evaluations when compared with state-of-the-art methods.

Adaptive constrained population extremal optimisation-based robust proportional-integral-derivation frequency control method for an islanded microgrid

The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency control.To alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID controller.During the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation performance.Then,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling technique.The proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric uncertainties.Finally,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms-based RPIDF controllers.Moreover,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.