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This article considers Nash equilibrium (NE) seeking of networked mean field games (NMFGs) with event-triggered strategies in a fully distributed manner. To simplify the formulation of distributed online decision-making problems over a network, we introduce the NMFG over a spatiotemporal domain for large-scale equilibrium computation. By merging ideas from estimation theory and consensus theory, three parallel auxiliary variables are designed to dynamically estimate the aggregative behavior of the collection, called the mean field term. The leaderless consensus of players’ estimates and the optimization of local objective function need to be achieved simultaneously. In addition, the dynamic event-triggered mechanism and adaptive learning gain are introduced, which provide a natural and scalable framework for distributed communication and computation in large-scale complex networks. We further prove that convergence to NE can be guaranteed using the designed strategies. Finally, a numerical experiment is provided to validate the proposed algorithm.

This article investigates for the first time the predefined-time relative position tracking and attitude synchronization control problem with asymptotic tracking errors for spacecraft. Compared to previous studies, this article considers convergence speed and control accuracy simultaneously, ensuring the predefined-time stability and asymptotic convergence of the spacecraft relative tracking errors. The fuzzy logic systems are introduced to estimate the unknown nonlinear terms in the relative dynamic model. By combining the adaptive backstepping control method and the command filter technique, a relative position tracking and attitude synchronization control method is proposed. The improved filter compensation signals are designed to eliminate the impact of filtering errors on the control performance. With the proposed control method, the closed-loop spacecraft position and attitude control system can achieve predefined-time stability, and the spacecraft relative tracking errors can reach zero as time approaches infinity. Finally, simulation results are provided, fully demonstrating the effectiveness of the proposed method.

This article investigates the fixed-time attitude control problem of quadrotor unmanned aerial vehicles (QUAVs) subject to stochastic disturbances. A fixed-time stability criterion for stochastic systems, which can compress the upper bound of the convergence time compared with the fixed-time stable Lyapunov theorem commonly used in the existing works, is proposed and theoretically proofed. The impact of stochastic disturbances on a QUAV is considered and a stochastic model of the attitude system of a QUAV is established. Based on the proposed criterion, a novel fixed-time attitude tracking controller for QUAV is designed, which can theoretically ensure the stability of attitude tracking within a fixed-time frame. To demonstrate the effectiveness of the controller, a simulation example is presented. A simulation comparison of different methods is also conducted. By artificially adding stochastic disturbances to the attitude angular velocity signals from sensors, an experiment is carried out on the three-DoF QUAV experimental platform. The experimental results show the feasibility of the designed control method in practical application.

This article is to develop an optimized attitude control using reinforcement learning (RL) for a quadrotor unmanned aerial vehicle (QUAV) system. Since this optimized scheme is required to steer both attitude angle position and velocity states to follow the predefined reference signals, it is a very challenging and interesting work. In theory, an optimal control can be found via solving the Hamilton–Jacobi–Bellman (HJB) equation. Nevertheless, the HJB equation associated with the QUAV attitude dynamic equation is hardly solved via analytical methods owing to the complex nonlinearity. To address the problem, the RL strategy of critic-actor architecture using fuzzy logic system approximation is proposed. For smooth manipulating both attitude angle position and velocity states, the optimized control has to contain both tracking error terms corresponding to the angle position and velocity. Thus, if the optimized attitude control learns from the traditional methods, the control algorithm will be very intricate. Since this RL tuning laws are derived from the negative gradient of a simple positive function that is equivalent to the HJB equation, the control algorithm is significantly simplified. Finally, both theory proof and actual experiment demonstrate that this optimized attitude control is competent for fulfilling the control objective.

Existing studies on table-based fact verification generally capture linguistic evidence from claim-table subgraphs or logical evidence from program-table subgraphs independently. However, there is insufficient association interaction between the two types of evidence, which makes it difficult to obtain valuable consistency features between them. In this work, we propose heuristic heterogeneous graph reasoning networks (H2GRN) to capture the shared consistent evidence by strengthening associations between linguistic and logical evidence from two perspectives of graph construction and reasoning mechanism. Specifically, 1) to enhance the close connectivity of the two subgraphs, rather than simply connecting two subgraphs by the nodes with the same content (the constructed graph in this way has severe sparsity), we construct a heuristic heterogeneous graph, which relies on claim semantics as heuristic knowledge to guide the connections of the program-table subgraph, and in turn expands the connectivity of the claim-table subgraph through logical information of programs as heuristic knowledge; and 2) to establish adequate association interaction between linguistic evidence and logical evidence, we design multiview reasoning networks. In detail, we propose local-view multihop knowledge reasoning (MKR) networks to enable the current node to establish association not only with one-hop neighbors, but also with multihop neighbors, to capture context-richer evidence information. We execute MKR on heuristic claim-table and program-table subgraphs to learn context-richer linguistic evidence and logical evidence, respectively. Meanwhile, we develop global-view graph dual-attention networks (DAN) that execute on the entire heuristic heterogeneous graph, reinforcing global-level significant consistency evidence. Finally, the consistency fusion layer is devised to weaken the disagreement between the three types of evidence to assist in capturing consistent shared evidence for verifying claims. Experiments on TABFACT and FEVEROUS demonstrate the effectiveness of H2GRN.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents’ operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.