Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

This work focuses on distributed time-varying optimization algorithms that can converge in a prescribed time period, both single-integrator systems and double-integrator systems are considered. A nested structure is proposed for applying prescribed-time approach to distributed time-varying optimization problems in this work. For single-integrator systems, the prescribed time interval is divided into three sub-intervals, then the average consensus estimation, the state consensus, and the optimized trajectory tracking are achieved sequentially through the time-scale function in the three sub-time intervals. This nested structure and the properties of the time-scale function ensure that the first-order algorithm is continuous and bounded. Therefore, the algorithm can be extended to double integrator systems by tracking the virtual first-order input signal. The validity of the proposed first-order and second-order algorithms is verified through optimal dynamic trajectory tracking experiments for indoor UAV clusters.

This paper uses a time-varying state feedback control method to investigate the global asymptotic stabilization issue of discrete-time switched systems with dwell-time constraints. A discrete dwell-time partitioning technique is proposed to design a time-varying Lyapunov function, which has a distinct characteristic that its value decreases at any time, even at each switching instant. Applying the partitioning technique and the time-varying control method, some new conditions with adjustable computational complexity are derived for stabilizing the discrete-time switched systems. Moreover, the extension to $l_2$-gain computation is presented in the sequel. Four examples are provided to illustrate the merits of the theoretical analysis.

This study focuses on $H_{\infty }$ security control for 2-D switched time delay systems subject to stochastic cyber attacks by the Roesser model, where the delay time-variation rate can be arbitrary. First, the time-delay system is modeled as the feedback interconnection of the delay-less system. To guarantee the security of the system under stochastic cyber attacks, a state-dependent switching signal and an anti-deception attack controller are constructed. Stability conditions of 2-D delay-less feedback interconnection systems are given for the first time. Then, for the considered system, some criteria based on bilinear matrix inequalities (BMIs) are established via the Lyapunov function technology such that the mean-square asymptotic stability with $H_{\infty }$ performance is guaranteed under arbitrary delay time-variation rate. Moreover, a cone-complement linearization (CCL) method is proposed to compute the optimized controller gains for BMIs. The effectiveness of the proposed method is validated via two numerical simulations.

Multi and hyperspectral images have become invaluable sources of information, revolutionizing various fields such as remote sensing, environmental monitoring, agriculture and medicine. In this expansive domain, the multi-linear mixing model (MMM) is a versatile tool to analyze spatial and spectral domains by effectively bridging the gap between linear and non-linear interactions of light and matter. This paper introduces an upgraded methodology that integrates the versatility of MMM in non-linear spectral unmixing, while leveraging spatial coherence (SC) enhancement through total variation theory to mitigate noise effects in the abundance maps. Referred to as non-linear extended blind end-member and abundance extraction with SC (NEBEAE-SC), the proposed methodology relies on constrained quadratic optimization, cyclic coordinate descent algorithm, and the split Bregman formulation. The validation of NEBEAE-SC involved rigorous testing on various hyperspectral datasets, including a synthetic image, remote sensing scenarios, and two biomedical applications. Specifically, our biomedical applications are focused on classification tasks, the first addressing hyperspectral images of in-vivo brain tissue, and the second involving multispectral images of ex-vivo human placenta. Our results demonstrate an improvement in the abundance estimation by NEBEAE-SC compared to similar algorithms in the state-of-the-art by offering a robust tool for non-linear spectral unmixing in diverse application domains.