IEEE Transactions on Cybernetics最新文献

The topology of a communication system is crucial in determining data transmission. Although significant research has been conducted on the integration of control and communication, existing studies on communication for control systems predominantly emphasize control aspects and warrant further exploration. Furthermore, there is a lack of research on the impacts of topology changes on control systems. This article aims to establish a connection between control and communication via communication topology, examining how communication topologies affect controllers. This article also analyzes the relationship between communication and control in depth. For static topologies, specific controller forms are derived from a general controller to illustrate the impacts of static topologies on controllers. In dynamic topologies, communication is nondeterministic, so whether a controller can receive data from other nodes is nondeterministic. Therefore, controller forms in which some coefficients are random variables following a probability distribution are derived. We utilize them to establish a close connection between control and communication. Furthermore, extensive simulations are conducted to investigate the impact of different topologies on the control system.

This article addresses the resilient cooperative optimal output regulation (COOR) control problem for nonlinear strict-feedback multiagent systems (MASs) under denial-of-service (DoS) attacks. By constructing the resilient adaptive distributed observers, the leader's dynamics and states can be estimated by each follower. In the control design, a control input constructed by feedforward and feedback control input is proposed based on the system data. Neural networks (NNs) are employed to learn solutions of the feedforward and optimal feedback control problems. Meanwhile, to handle the influence caused by unknown nonlinear dynamics, combining off-policy integral reinforcement learning (IRL) algorithm with actor-critic NNs (A-C NNs), an optimal feedback security control law is designed. To illustrate the feasibility and effectiveness of the proposed optimal control strategy, numerical and practical simulation examples are provided. Unlike prior studies limited to linear systems, this work explicitly accounts for complex nonlinear dynamics, significantly broadening the applicability of resilient COOR control problem in real-world applications.

Multiagent reinforcement learning (MARL) has garnered extensive research attention due to its strong learning capabilities, leading to its deployment in increasingly challenging scenarios. Although progress has been made toward more generalizable solutions, many MARL algorithms continue to struggle with balancing scalability and heterogeneity, particularly under conditions of growing uncertainty. Research has shown that combining dense local interactions with sparse global interactions can significantly enhance scalability while preserving agent heterogeneity. Motivated by these insights and inspired by human social behavior, we propose a novel hierarchical method that integrates human guidance with multiagent systems (MASs). Rather than requiring agents to learn from scratch, our method transfers abstract knowledge from humans, employing fuzzy logic to manage the inherent uncertainty in this guidance and reduce the required human effort. To accommodate both local and global interactions, we introduce two levels of human guidance: individual action guidance for agents and an attention graph to describe agent relationships. Our proposed approach is end-to-end and compatible with diverse MARL algorithms. We evaluate our approach in the starcraft multiagent challenge (SMAC) and SMACv2 environments. Empirical results demonstrate its effectiveness, even under low-performance fuzzy human guidance.