This paper develops a Neural Network (NN) event-triggered finite-time consensus control method for uncertain nonlinear Multi-Agent Systems (MASs) with dead-zone input and actuator failures. In practical applications, actuator failures would inevitably arise in MASs. And the time, pattern, and value of the failures are unknown. Besides, the actuators of MASs also suffer from dead-zone nonlinearity. No matter actuator failures or dead-zone input would dramatically affect the performance and stability of MASs. To address these issues, finite-time adaptive controllers capable of simultaneously compensating for actuator failures and dead-zone input are constructed by adopting the backstepping technology. Meanwhile, the NN control scheme is adopted to handle the unknown nonlinear dynamics of each agent. Furthermore, an event-triggered control mechanism is established that no longer requires continuous communication on the control network. Under the proposed control method, all followers achieve finite-time synchronization, irrespective of the presence of limited bandwidth, unknown failures, and dead-zone input. These results are demonstrated by simulations. 

NN event-triggered finite-time consensus control for uncertain nonlinear Multi-Agent Systems with dead-zone input and actuator failures.

This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results.

Event‐triggered security control for fuzzy‐model‐based cyber‐physical systems under Denial‐of‐Service attacks and actuator faults

This paper focuses on the finite-time tracking control problem of fractional-order multi-agent systems subject to input saturation and constraints. The interaction topology is assumed to be directed and contain a spanning tree. The appropriate barrier Lyapunov functions are constructed to tackle the output and partial states constraints. Since only the system output is available, a reduced-order state observer is constructed to obtain the unmeasurable state variables. Fuzzy logic system is applied to tackle the uncertain nonlinear dynamics in the system and the unknown parameters are estimated by adaptive laws. An event-triggered control scheme is designed to reduce communication burden. The proposed distributed controller can guarantee that all signals of the system are bounded, the constrained states never breach the time-varying constraints, finite-time tracking can be achieved with a bounded error and the Zeno behavior does not occur. At last, the effectiveness of the proposed control scheme is validated by an example. 

Event-Triggered Finite-Time Tracking Control for Fractional-Order Multi-Agent Systems with Input Saturation and Constraints

The impulsive consensus problem of nonlinear multi‐agent system under energy consumption constraint is considered in the article. For considering the restricted communication channel, asymmetrical input saturation constraint is adopted into impulse control protocol. Then, based on the current national policy of energy conservation and emission reduction, a novel control protocol which combines asymmetrical input saturation constraint impulse protocol with event‐triggered approach is raised and set event decision time at pulse time. Then, the control protocol not only can be able to decrease communication frequency and energy consumption, but also avoid “Zeno behavior”. Furthermore, some theorems and their proofs about the consensus problems with and without leader are provided. Through mathematical derivation, sufficient conditions of problems have been crystallized. In the end, some different simulation instances are presented to verify the research.

Event‐triggered asymmetrical saturation‐constraint impulsive control of multi‐agent systems

Abstract This article investigates the problem of fixed‐time sliding mode consensus tracking for nonlinear second‐order multi‐agent system, in the presence of disturbances and actuator attacks. The interaction topology graph of multi‐agent system is directed and switched, and the switching topologies satisfy a more general persistent dwell‐time switching mechanism. To account for the disturbances, actuator attack, and switching topologies, a nonsingular sliding mode control law with distributed switching scheme is constructed to guarantee the fixed‐time reachability to a sliding surface. By using multiple Lyapunov functions, some new sufficient criteria are derived for fixed‐time consensus tracking of the multi‐agent system with persistent dwell‐time switching topologies. To illustrate the proposed approach, we present a numerical example and a physical example of the single pendulum model. 

Fixed‐time sliding mode consensus tracking for second‐order nonlinear multi‐agent system with persistent dwell‐time switching topologies

Multi‐agent system (MAS) is a common cyber‐physical system (CPS). Due to it often uses a relatively open network platform, it is vulnerable to malicious cyber attacks in the process of system operation, so the control scheme design is critical to ensure the system operation security. In this article, we propose a new resilient neuroadaptive dynamic surface control scheme for non‐linear MASs with potential cyber attacks (false data injection and denial‐of‐service), system uncertainty, unknown control gain, and output constraints which are usually seen on CPSs. In this scheme, the neuroadaptive controller design ensures that all signals of the closed loop system are semi‐globally uniform ultimately bounded when the MAS even exists time‐varying cyber attacks on data links among agents, and the links between controllers, actuators, and sensors. Using Gaussian radial basis function neural network, the system uncertainty, non‐strict feedback terms, unknown control gain, and some cyber attacks are effectively solved and the process of controller design is extremely simplified. Finally, we provide a simulation result to verify the effectiveness and superiority of the proposed control scheme.

Consensus tracking control for uncertain non‐strict feedback multi‐agent system under cyber attack via resilient neuroadaptive approach

This article proposes a distributed neuroadaptive monitoring fault‐tolerant consensus control scheme for a class of uncertain, nonlinear, strict feedback multi‐agent systems which have actuator faults and all the control coefficients in them are unknown. This scheme provides each agent with a local monitor combined with the actuator switching to solve actuator failure. Simultaneously, it guarantees the tracking error satisfies the prescribed transient and steady‐state performance, even if there exist actuators switching. Furthermore, the time varying asymmetric Barrier Lyapunov function (BLF) and the auxiliary system are used to analyze input and output constraints' influence. Under the action of aforementioned control scheme, closed‐loop systems can be stable and semiglobal uniform boundedness. Additionally, its efficacy can be proved in numerical simulation.

Distributed neuroadaptive monitoring fault‐tolerant consensus control for nonlinear multi‐agent systems with actuator faults

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ming Jin

Author Tools

Chat about Author

Papers

Consensus tracking control for uncertain non‐strict feedback multi‐agent system under cyber attack via resilient neuroadaptive approach

Distributed neuroadaptive monitoring fault‐tolerant consensus control for nonlinear multi‐agent systems with actuator faults