Zhi Li

For second-order multi-agent systems with a directed spanning tree topology, this paper proposes a distributed prescribed-time control algorithm based on a novel time-varying function and derives a general parameter calculation formula. This formula significantly reduces the parameter tuning complexity compared to traditional methods that construct time-varying gains using inverse proportional functions. Building on this, a distributed prescribed-time formation tracking protocol is further designed within the distributed control framework. This protocol not only avoids the design overhead associated with observers in traditional tracking control but also effectively suppresses the control input amplitude during the initial system stage. Theoretical analysis demonstrates that even in the presence of bounded disturbances, the protocol can still ensure stable formation within the prescribed time, thereby verifying the robustness of the algorithm. Finally, formation tracking simulation experiments fully validate the effectiveness and superiority of the proposed approach.

The integration of Federated Learning (FL) with the Internet of Things (IoT) represents an advanced technological trend, combining the extensive connectivity of IoT with the powerful processing capabilities of FL to drive innovation and optimization across multiple domains. Given the heterogeneity of IoT devices and the variability in data distribution, developing strategies to enhance FL performance without overly burdening resource-constrained devices is crucial. This paper proposes a FL algorithm based on Generative Artificial Intelligence (GAI) for IoT devices with extreme heterogeneity in data and resources. The algorithm utilizes pre-trained GAI models to generate new data, aligning the data distributions of individual IoT devices closer to independent and identically distributed (i.i.d.), thereby effectively reducing the heterogeneity of local data. Additionally, the proposed algorithm incorporates data synthesis and resource scheduling strategies to mitigate the heterogeneity of local device resources. Finally, we formulate a joint optimization problem aimed at minimizing total energy consumption while maximizing FL performance. Experimental results demonstrate that, under significant resource and data distribution disparities, most existing solutions struggle to converge, whereas the proposed method converges and achieves superior performance. Compared to existing GAI-based approaches, our method significantly reduces latency and energy consumption.

In structured populations, the ecology of games may vary over neighborhoods. The effect of the ecological variations on population dynamics remains largely unknown. We here incorporate the ecological variations into the set-structured populations to explore the coevolutionary dynamics of the ecology and cooperation. Individuals of a population are distributed over sets. Interactions occur in the form of evolutionary games. When two individuals share more common sets, they play the weak prisoner’s dilemma. Otherwise, they play the strong prisoner’s dilemma. Both the set memberships and the strategy update in the evolutionary process. Changes in set memberships hold sway over the games to be played, which, in turn, influences the performance of strategies. Combining evolutionary set theory and random walks on graphs, we derived the conditions for cooperation to be selected under the weak selection limit. We find that a denser set-structured population increases the probability of individuals participating in a weak prisoner’s dilemma, and thereby promoting the spread of cooperation. Properly modulating the population structure and the payoff feedback can further lower the critical benefit–cost ratio required for cooperation to be selected. Our results may help better understand the effects of ecological variations in enhancing cooperative behavior in set-structured populations.

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system’s topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations.

This paper investigates the problem of fixed-time event-triggered consensus control for a class of multi-agent systems with disturbed and non-linear dynamics. A fixed-time consensus protocol based on an event-triggered strategy is proposed, which can ensure a fixed-time event-triggered consensus, reduce energy consumption, and decrease the frequency of controller updates. The control protocol can also be applied to the case when the systems are free of disturbances; it solves the problem of high convergence time of the systems and reduces energy consumption of the systems. Sufficient conditions are proposed for the multi-agent systems with disturbed and non-linear dynamics to achieve the fixed-time event-triggered consensus by using algebraic graph theory, inequalities, fixed-time stability theory, and Lyapunov stability theory. Finally, simulation results show that the proposed control protocol has the advantages of both event-triggered and fixed-time convergence; compared to previous work, the convergence time of the new control protocol is greatly reduced (about 1.5 s) and the update times are also greatly reduced (less than 50 times), which is consistent with the theoretical results.