Distributed Multiobjective Optimization for Network Resource Allocation of Multiagent Systems

Distributed Predefined-Time Convergent Algorithm for Solving Time-Varying Resource Allocation Problem Over Directed Networks

This article introduces an innovative distributed algorithm tailored for achieving predefined-time convergence in addressing time-varying resource allocation problem under directed networks. The attainment of predefined-time convergence is crucial for fulfilling real-time requirements, ensuring quality and safety standards, and optimizing the efficiency of resource utilization. It grants users the flexibility to tailor the convergence time according to their specific requirements and constraints. Moreover, the algorithm integrates an auxiliary system to ensure continual satisfaction of the global equality constraint. A distinctive feature lies in the utilization of nonhomogeneous functions with exponential terms, facilitating the achievement of predefined-time convergence. Compared to some existing algorithms with dynamic behaviors, including asymptotical convergence, exponential convergence, and fixed-time convergence, the proposed algorithm demonstrates superior convergence speed. Finally, we demonstrate the effectiveness of the designed technique through numerical simulations, comparisons with state-of-the-art algorithms, and its application to multienergy management problem in the multimicrogrid system.

Distributed Privacy-Preserving Optimization With Accumulated Noise in ADMM

Privacy preservation for distributed optimization in multiagent systems has been widely concerned in recent years. In this article, the accumulated noise privacy-preserving alternating direction method of multipliers (ANPPM) algorithm is proposed to preserve the private information of each agent. The masked states of each agent are sent to its neighbors with a designed noise-adding mechanism, and an accumulated term is introduced to confuse the gradients at each iteration. With ANPPM, all the agents can achieve privacy preservation for the information of real states and subgradients. Moreover, the states of all the agents can be guaranteed to converge to the optimal solution. The convergence rate of is consistent with standard ADMM, hence no adverse effect is induced by the privacy-preserving mechanism. Numerical results are provided to validate the effectiveness of the proposed ANPPM algorithm.

Parametric Dynamic Distributed Containment Control of Continuous-Time Linear Multi-Agent Systems with Specified Convergence Speed

This paper focuses on the distributed containment control of continuous-time linear multi-agent systems (MASs) with multiple leaders over fixed topology. A parametric dynamic compensated distributed control protocol is proposed in which both the information from the observer in the virtual layer and actual adjacent agents are employed. The necessary and sufficient conditions of the distributed containment control are derived based on the standard linear quadratic regulator (LQR). On this basis, the dominant poles are configured by using the modified linear quadratic regulator (MLQR) optimal control and Geršgorin's circle criterion, hence the containment control with specified convergence speed of the MAS is achieved. Another main advantage of the proposed design is, in the case of virtual layer failure, by adjusting parameters the dynamic control protocol reduces to static, and the convergence speed can still be specified through the dominant pole assignment method combined with inverse optimal control. Finally, typical numerical examples are presented to demonstrate the effectiveness of theoretical results.