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

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.

This paper investigates the trajectory tracking control issue for a linear parameter-varying (LPV) system of a wheeled mobile robot (WMR) with actuator fault and constraints, where a time-varying intermediate estimator (TVIE)-based fault-tolerant model predictive control (MPC) method is proposed. To obtain the real system with actuator fault, based on the existing traditional IE, an optimized objective function is designed to update its observer gain online, which results in a TVIE. A new estimation-based predictive model in MPC is designed by introducing the estimations of error state and fault from the TVIE. Moreover, compared with the existing IE or extended state observer (ESO), the estimation performance of TVIE is better. Then the MPC optimization strategy is used to design fault-tolerant controllers. To guarantee stability and recursive feasibility, the bimodal MPC strategy is used, where a terminal penalty and a terminal constraint are included. Finally, the effectiveness and superiority of the proposed approach is illustrated in experiment.

This article mainly addresses the $H_{\infty }$ load frequency control issue for multi-area power systems with multi-time-varying delays, where a sliding mode control scheme is developed to make the objective system more robust. Considering that the data transmission from the sensor to the controller in the channel may be vulnerable to malicious attacks, the deception attacks are taken into account in the controller design to ensure data authenticity while maintaining the system performance. By constructing a system model regarding multi-time-varying delays in multi-area power systems, this article aims to design a reliable sliding mode controller that guarantees the objective system asymptotic stability with an expected $H_{\infty }$ performance. Then, through the linear matrix inequality technology, sufficient conditions with less conservativeness maintaining the system asymptotic stability with a set of proper controller gains are established.Finally, a two-area interconnected power system is used to validate the presented method.

In this article, the practical fixed-time fault-tolerant consensus problem of a class of first-order nonlinear multiagent systems with actuator faults is investigated. Owing to the existence of uncertain actuator failures (multiplicative faults and additive faults) and non-linearities, it is challenging to study the fixed-time consensus problem of such multiagent systems in a fully distributed approach. To get over this challenge, a fully distributed adaptive protocol that utilizes adaptive techniques is proposed. Under this control protocol, multiagent systems can achieve consensus, and by adjusting controller parameters, the consensus error can converge to the acceptable bounded region in a fixed time. In addition, due to the introduction of adaptive parameters, the design of the control protocol is not dependent on any global network topology information, which means the control protocol is fully distributed. Therefore, the proposed scheme has better scalability and wider applicability. Finally, the simulation results are proposed to validate the fault-tolerant ability and fully distributed characteristics of the presented scheme.