Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs’ mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent’s time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach’s effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking

Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks

This paper studies fully distributed data-driven problems for nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network-based detection and compensation schemes are developed. Rigorous convergence proof is presented that the bipartite consensus error is ultimately boundedness. Finally, the effectiveness of the designed method is verified through simulations and experiment

Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method

Responsibility of education in improving medical college students’ ability to prevent and respond to public health emergencies in China – A systematic review

BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has highlighted the critical importance of sufficient preparedness for public health emergencies. This places higher requirements on the ability of medical staff to deal with such emergencies. Nonetheless, education courses on public health emergencies in China are usually aimed at public health students, and not at all medical college students. Importantly, these medical students will become medical workers who are generally the first-contact personnel and play an irreplaceable role in responding to most public health emergencies. Therefore, it is urgent to strengthen educational courses to enable these students to adequately prevent and respond to public health emergencies.ObjectivesThe purpose of this systematic review was to reveal the current unsatisfactory status of Chinese medical college students’ knowledge and skills in dealing with public health emergencies and their training needs.MethodsWe searched EMBASE, PubMed, Google Scholar, Web of Science, CNKI, Wan Fang, and VIP Information Network for all associated original studies written in English and Chinese from the inception of these databases until March 12, 2022.ResultsThis systematic review screened out 15 eligible studies that met the inclusion criteria. These studies demonstrated that Chinese medical college students generally have a low ability to deal with public health emergencies. Most students believe it is essential to master coping with public health emergencies and desire to acquire this knowledge. But the participation rate is low, and only a few students actively seek relevant knowledge.ConclusionThe findings of this review illustrate the importance of improving medical college students’ education to prevent and deal with public health emergencies. It is necessary to improve medical college students’ education in responding to public health emergencies.Systematic Review Registration: PROSPERO, Identifier [CRD42023467374]

The sheaf representation of residuated lattices

The residuated lattices form one of the most important algebras of fuzzylogics and have been heavily studied by people from various different points ofview. Sheaf presentations provide a topological approach to many algebraicstructures. In this paper, we study the topological properties of primespectrum of residuated lattices, and then construct a sheaf space to obtain asheaf representation for each residuated lattice.Comment: This is a paper presented at ISDT

A novel medium for long-term primary culture of hemocytes of Metapenaeus ensis

The development of a suitable shrimp cell medium is essential for achieving a long-term cell culture and finite cell line of shrimps routinely. In this study, we have successfully developed an optimal shrimp cell medium that can be used for long-term in vitro culture and continuous subculture of the hemolymph cells (or hemocytes) of greasyback shrimp Metapenaeus ensis, designated as MeH cells, by shrimp serum-based and supplements-based optimization of the basic and growth medium. In this article, we have focused on the details for the preparation of the optimal shrimp cell medium by diluting and mixing of various stock solutions as well as the methods for isolation and primary culture of MeH cells. • A novel shrimp cell growth medium is developed for long-term shrimp hemocytes culture. • The preparation method of shrimp cell growth medium is successfully established. • Obvious cell activity and proliferation potential of isolated shrimp cells can be maintained beyond 30 days

Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems

This paper considers the data quantization problem for a class of unknown nonaffine nonlinear discrete-time multi-agent systems (MASs) under repetitive operations to achieve bipartite consensus tracking. Here, a quantized distributed model-free adaptive iterative learning bipartite consensus control (QDMFAILBC) approach is proposed based on the dynamic linearization technology, algebraic graph theory, and sector-bound methods. The proposed approach doesn’t require each agent’s dynamics knowledge and only uses the input/output data of MASs, where the data is coded by the logarithmic quantizer before being transmitted. Moreover, we consider both cooperative and competitive relationships among agents. We rigorously prove the stability of the proposed scheme and analyze the effects of data quantization. Meanwhile, we demonstrate that data quantization does not affect the stability of MASs, and bipartite consensus tracking errors can converge to zero with the processing of the proposed scheme, although the data quantization slows the convergence rate. Furthermore, the results are extended to switching topologies, and three simulation studies further validate the effectiveness of the designed metho

Model-free adaptive consensus tracking control for unknown nonlinear multi-agent systems with sensor saturation

This article proposes a distributed model-free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete-time multi-agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time-varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results