Direct current microgrids (DCmGs) are attractive due to their high efficiency and ease of deployment, yet decentralized controllers remain vulnerable to false-data-injection (FDI) attacks and abrupt load changes. This article develops a resilient decentralized control architecture that achieves performance guarantees under bounded FDI attacks using only local measurements. The key idea is a decentralized high-gain observer that reconstructs each power buffer input impedance and stored energy while explicitly accounting for network coupling. On this basis, a smooth nonlinear feedback term provides attack compensation without chattering. In contrast to existing schemes, our design comes with explicit linear matrix inequality conditions that, first, certify asymptotic stability in the attack-free case and, second, ensure uniform ultimate boundedness of the closed loop under FDI, thereby yielding transparent tuning ranges for the controller and observer gains. Hardware-in-the-loop experiments, in which the DCmG is subjected to staged FDI attack windows and load steps, are conducted to demonstrate the proposed design with robust and practically tunable resilience for DCmGs.

This article addresses the stabilization challenges of nonholonomic systems under the threat of false data injection (FDI) attacks, which compromise the integrity of state information. A novel adaptive control strategy using Nussbaum-type gains is proposed to ensure the asymptotic stability of the closed-loop system while maintaining signal boundedness. The approach extends conventional Nussbaum designs to handle multiple unknown control directions. It integrates online learning mechanisms to mitigate the impact of FDI attacks. Additionally, adaptive backstepping and fuzzy-logic systems are utilized to approximate and compensate for unknown nonlinear dynamics. The methodology transforms nonholonomic systems into equivalent cascade structures to address inherent constraints and enable secure control input design. Simulation studies validate the effectiveness and resilience of the proposed control strategy, demonstrating significant improvements in stability and robustness in the presence of FDI attacks.

This article proposes an event-based asymptotic tracking control method for nonlinear strict-feedback systems with multiple unknown control directions. The system is characterized by multiple unknown control directions, which pose challenges to its performance. In contrast to traditional Nussbaum-type methods, we propose a novel Nussbaum-type function to handle multiple Nussbaum-type gains, ensuring robust asymptotic tracking. Additionally, two event-triggered mechanisms are developed to alleviate the computational complexity of adaptive Nussbaum design. The static event-triggered mechanism significantly improves the system’s responsiveness to dynamic changes by employing dynamically decreasing thresholds. Building on this, a dynamic event-triggered mechanism is introduced, incorporating an internal variable that continuously adjusts the triggering conditions over time. Furthermore, the proposed design not only achieves asymptotic tracking control but also ensures that both event-triggered mechanisms avoid the Zeno phenomenon. To validate the proposed design schemes, a simulation example of a marine surface vehicle is presented.

In this article, we propose an adaptive fuzzy control design for the distributed competitive control problem of multiagent systems (MASs) with multiple unknown control directions. The bipartite synchronization control is investigated by using the fuzzy backstepping control framework and fuzzy logic systems. To broaden the application field for the distributed protocol design, we consider practical bipartite synchronization for a group of MASs consisting of followers subject to heterogeneous unknown control directions. To address these multiple unknown control directions, a novel Nussbaum-type function is developed. Moreover, to reduce the communication bandwidth, this article proposes two threshold strategies for event-triggered control to avoid any unnecessary sampling while taking flexibility into consideration, further improving the efficiency and feasibility of the developed bipartite protocol design. The experimental results indicate that the proposed control method can effectively realize bipartite synchronization of MASs with multiple unknown control directions.

Traffic supply-demand mismatching has a severe impact on intelligent transportation systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply-demand imbalance. Along this line, this article proposes the Multi-Driver Multi-Order Dispatching (MDMOD) method to make efficient order dispatching policy and enhance the experience of drivers and passengers. In the proposed MDMOD method, the Dynamic Multi-Objective Reward Learning (DMRL) algorithm is proposed to measure the driver-order-pair value, which illustrates the importance of a driver serving a specific order. A centralized matching algorithm is introduced to match all drivers and orders to maximize all driver-order-pair values. The multi-objective reward in the DMRL algorithm considers both immediate gains (i.e., pick-up distance) and future gains (i.e., the future traffic demand of order destination) to effectively improve the experience of drivers and passengers. Furthermore, by introducing the driver service level into the multi-objective reward, the “outstanding driver better reward” mechanism is realized to promote the ecological development of ride-sharing platforms. Notably, the Temporal-Graph Convolutional Network algorithm is proposed to predict the future traffic demand. Some virtual orders, which generated with the predicted future traffic demand, are dispatched to idle drivers to multiplex the traffic supply fully. A simulator is designed to test the performance of the proposed MDMOD method, experimental results demonstrate that the MDMOD method outperforms the state-of-the-art methods in terms of Average Driver Income and Order Response Rate.

This paper studies quasi-discrete closure spaces and fuzzy closure spaces. We show that any topological closure cT induced by a closure c is the smallest extension from a closure space to a topological closure space in both crisp and fuzzy environmen

This paper proposes two different covering reduction algorithms by means of Boolean matrices. We define a dual notion of product on Boolean matrices and establish the relationship between characteristic matrix of a covering and relational matrices of two covering-induced relations, i.e. the minimum and the maximum relations in a covering. This paper shows that relational matrices of minimum and maximum relations in a covering can be written as intersection and union of many “elementary” matrices and each “elementary” matrix is corresponding to one element in the covering, respectively. Finally, as an application of this result, we propose two types of covering reduction algorithms.

A topological space is a “space”, where “near” makes sense; it is formally defined by the Topological Neighborhood System (TNS). Here, we explore the concept of “conflict” by the system of “Anti-TNS”; by that we mean a “mathematical structure” that consists of a set of “punctured” neighborhoods, namely, the center point p of all neighborhoods of TNS has been removed. “Conflicts” are important concepts in computer security. The primary results is the axiomatization of ATNS. The main results are surprising: The set of the axioms is the same as that of topological spaces. Similar results for pretopological spaces also are obtained.