Role-based collaboration has proven to be a promising approach to addressing collaboration issues. Environments-classes, agents, roles, groups, and objects (E-CARGO) is a formalized model of it. Group multi-role assignment is core to such collaboration, yet it is seriously constrained by cooperation and conflict relationships between agents and roles—a key practical challenge existing research fails to clearly address. Traditional studies mostly adopt predefined matrices to depict agent-role interactions, while lacking empirical support. To fill this gap, this paper adopts three-way decision theory, introduces agent preference, and employs the Spearman rank correlation coefficient to measure agent-role relevance. With set thresholds, it identifies internal cooperation and conflict relations, and further applies the method to optimize group multi-role assignment for conflict elimination and cooperation promotion. This paper formulates necessary constraint conditions and designs corresponding verification algorithms. Experimental and comparative results verify the effectiveness and practicality of the proposed model.
The distributed event-triggered consensus problem of linear systems is studied in this article. Unlike the existing event-triggered adaptive consensus protocols, where the control gain is composed of an adaptive weight and a feedback matrix, this article proposes an integrated adaptive event-triggered control approach. The control gain matrix can be adaptively and intermittently updated according to the consensus behavior. It is proven that the developed event-triggered mechanism can reach consensus and avoid the Zeno behavior. Some simulation results are provided to illustrate the effectiveness of the proposed protocols.
Offshore wind power serves as a pivotal pillar of sustainable energy, with its operational and maintenance efficiency heavily dependent on effective human resource coordination. This paper proposes a dynamic personnel allocation optimization method based on hypergraph structures, constructing an RGF propagation model to characterize the many-tomany complex interactions between teams and tasks across multiple task categories. Unlike traditional graph models that can only represent pairwise relationships, the hypergraph framework captures group-level collaborations and spatiotemporal propagation effects, thereby more accurately simulating the dynamic evolution of personnel states. Furthermore, a reinforcement learning control framework based on Proximal Policy Optimization (PPO) is designed to achieve adaptive strategy optimization in uncertain environments. Numerical simulations and multibaseline comparative experiments validate the effectiveness of the proposed method: the PPO controller significantly reduces task completion time, personnel requirements, and overall costs, while demonstrating superior performance over traditional methods and other reinforcement learning algorithms in dynamic task environments. This framework provides a novel theoretical foundation for human resource scheduling and control optimization in large-scale offshore wind projects, offering substantial practical potential.
While reinforcement learning (RL) has advanced optimal control under limited resources, existing approaches often neglect two critical challenges in real-world engineering: switching communication topologies and time delays. This paper distinguishes itself by addressing the online adaptive optimal consensus control problem for multi-agent systems (MASs) that explicitly include these factors. The switching network topology is characterized by a Markov chain, capturing random changes, and a periodic switching signal, modeling scheduled variations. We first formulate a quadratic cost function to evaluate system performance and derive the associated Hamilton-Jacobi-Bellman (HJB) equation. A policy iteration (PI) method is established to solve this HJB equation offline. Leveraging this, a novel online adaptive optimal controller is proposed using a critic-actor neural network structure. This structure learns the optimal control policy in real-time without requiring knowledge of the system dynamics. The convergence of the policy iteration algorithm and the stability of the closed-loop system are rigorously analyzed. Finally, simulation examples are presented to demonstrate the efficacy and superiority of the proposed control mechanism.
