Consensus Of Nonlinear Multi-agent Systems Research Articles

Fault-tolerant consensus of nonlinear multi-agent systems with channel noises under multiple faults and denial-of-service attacks

Fault-tolerant consensus of nonlinear multi-agent systems with channel noises under multiple faults and denial-of-service attacks

Adaptive sliding mode consensus tracking for nonlinear leader-following multi-agent systems with distributed estimation strategy

This paper studies the consensus of nonlinear multi-agent system based on distributed estimation and adaptive sliding mode control schemes. A strategy for distributed estimation is proposed to ensure that every follower is able to estimate the state of the leader within a finite time. Then a structurally simpler integral sliding manifold is designed to eliminate the reaching phase. Furthermore, two control protocols are formulated to ensure the reachability of sliding surface according to the estimated state of the leader. The first protocol is a discontinuous one that ensures exponential convergence of the consensus tracking error to zero, but its switching gain is difficult to be adjusted. Then an adaptive protocol is developed to adjust the switching gain automatically, which is the second control protocol. This protocol not only avoids chattering but also relaxes the dependence on certain boundaries. Finally, simulations are conducted to validate the efficacy of the employed strategy.

Data-driven leader-following consensus for nonlinear multi-agent systems against composite attacks: A twins layer approach

This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including denial of service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside the traditional cyber–physical layer (CPL), inspired by the recent Digital Twin technology. Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL, and the other is resilient decentralized tracking control against actuation attacks on the CPL. First, a distributed observer based on switching estimation law against DoS is designed on TL. Second, a distributed model-free adaptive control (DMFAC) protocol based on attack compensation against AAs is designed on CPL. Moreover, the uniformly ultimately bounded convergence of consensus error of the proposed double-layer DMFAC algorithm is strictly proved. Finally, the simulation verifies the effectiveness of the resilient double-layer control scheme.

PD and PI Control for the Lag Consensus of Nonlinear Multiagent Systems With and Without External Disturbances.

In this article, we investigate the lag consensus and lag H∞ consensus problems for second-order nonlinear multiagent systems (MASs) by utilizing the proportional-derivative (PD) and proportional-integral (PI) control methods. On the one hand, a criterion is developed for ensuring the lag consensus of the MAS by choosing an appropriate PD control protocol. Moreover, a PI controller is also provided to guarantee that the MAS can achieve lag consensus. On the other hand, several lag H∞ consensus criteria are also given for the case in which external disturbances appear in the MAS; these criteria are developed by exploiting the PD and PI control strategies. Finally, the devised control schemes and the developed criteria are verified by employing two numerical examples.

A Hybrid Systems Approach to Consensus of Nonlinear Multi-agent Systems With Self-triggered Output Feedback Control

A Hybrid Systems Approach to Consensus of Nonlinear Multi-agent Systems With Self-triggered Output Feedback Control

Distributed disturbance observer-based nonfragile bipartite consensus of nonlinear multiagent systems with multiple time-varying delays

The paper concentrates on the issue of distributed disturbance observer-based nonfragile bipartite consensus within nonlinear delayed multiagent systems, encompassing both leaderless and leader-following structures. The delays under consideration are nonuniform, manifesting in the state, the nonlinearity, and the communication processes. To suppress the external disturbances and the observer gain perturbations, distributed nonfragile disturbance observers pertaining to relative output and communication delays are developed to estimate the external disturbances for each agent. Employing the developed disturbance observer, distributed control protocols for nonfragile bipartite consensus are constructed incorporating states, estimated disturbances, and communication delays. These protocols can ensure bipartite consensus, compensate for external disturbances, and tolerate uncertainties in control gain. New augmented Lyapunov-Krasovskii functions are formulated by introducing the triple integral term and the augmented vector. The new bipartite consensus criteria for the studied multiagent systems are established with less conservatism by employing the techniques on second-order Bessel-Legendre integral inequality, reciprocally convex combination, and free weight matrix. Finally, numerical simulations and comparisons are performed for both leaderless and leader-following scenarios, thereby validating and enhancing the theoretical outcomes.

Adaptive impulsive consensus of nonlinear multi-agent systems via a distributed self-triggered strategy

This article investigates the consensus problem of nonlinear leader-following multi-agent systems (MASs) via adaptive impulsive control. A distributed impulsive controller is employed, in which an adaptive updating law in the discrete-time setting is designed for the impulsive gain. Moreover, in order to reduce the communication cost, a distributed self-triggered strategy is proposed to determine when the impulsive instant occurs. Some criteria are derived to guarantee consensus of leader-following MASs based on Lyapunov stability theory and algebraic Riccati equation. It is proved that the self-triggered impulsive sequence does not exhibit Zeno behaviour. Finally, an illustrative example is presented to empirically validate the effectiveness of the theoretical results.

Consensus for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme

This paper studies the leader–follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggered mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems.

Stubborn Observer for Leader-Following Consensus of Nonlinear Multiagent Systems With Application to Spacecraft.

In this article, the leader-following consensus problem is investigated for a class of nonlinear multiagent systems with transmission outliers. First, through the relative output information and observer states of neighbor agents, a distributed stubborn reduced-order observer is proposed to estimate the leader-following consensus error which is affected by abnormal and isolated transmission outliers. Moreover, considering that the leader agent in practice is a controllable plant, another distributed stubborn reduced-order observer is designed by using the relative information and inputs of neighbor agents. Sufficient conditions are established in terms of the Laplacian matrix such that two stubborn reduced-order observers are convergent and the studied nonlinear multiagent system could achieve leader-following consensus with directed topology. Finally, an example of the multispacecraft system is provided to illustrate the effectiveness of the proposed methodology.

Output Feedback-Based Consensus for Nonlinear Multiagent Systems: The Event-Triggered Communication Strategy.

The current investigation explores the leader-following consensus problem for nonlinear multiagent systems under the output feedback control mechanism and the event-triggered communication mechanism. Owing to the physical instrument constraints, a significant portion of the state variables is not readily available. Therefore, this article put forward a distributed event-based leader-following consensus protocol only using agents' relative output measurements and underlying neighbors. Furthermore, this article develops two event-triggered mechanisms simultaneously, one is the event-triggered communication mechanism in the sensor-to-controller channel, and another is the event-triggered controller update in the controller-to-actuator track. Besides that, it is proven that the developed event-triggered control protocol can settle the leader-following consensus problem of the nonlinear multiagent systems, and the Zeno behavior is excluded in both the channels. Finally, we perform two simulation examples to illustrate the efficacy of the obtained results.

Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks

This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme’s effectiveness.

Sampled-Data Secure Group Consensus for Nonlinear Multi-Agent Systems in Fixed/Switching Topologies

Sampled-Data Secure Group Consensus for Nonlinear Multi-Agent Systems in Fixed/Switching Topologies

Prescribed-time cluster practical consensus for nonlinear multi-agent systems based on event-triggered mechanism.

This paper investigates the prescribed-time event-triggered cluster practical consensus problem for a class of nonlinear multi-agent systems with external disturbances. To begin, to reach the prescribed-time cluster practical consensus, a new time-varying function is introduced and a novel distributed continuous algorithm is designed. Based on the Lyapunov stability theory and inequality techniques, some sufficient conditions are given, ensuring the prescribed-time cluster practical consensus. Moreover, to avoid different clusters' final states overlapping, a virtual leader is considered for each cluster. In this case, an event-triggered distributed protocol is further established and some related conditions are given for achieving prescribed-time cluster practical consensus. Additionally, it is proven that the Zeno behavior can be avioded by choosing parameters appropriately. Finally, some numerical examples are presented to show the effectiveness of the theoretical results.

Event-triggered and stochastic-sampled consensus of nonlinear multi-agent systems under semi-Markov switching topology

This article investigates exponential mean square consensus (MSC) problem of leader–follower multi-agent systems (MASs). It is considered that the communication topology switches randomly and obeys a semi-Markov process. In view of possible data loss in information transmission, stochastic sampling scheme is introduced. Meanwhile, mode-dependent event-triggered control strategy based on sampled data is adopted to decrease communication broadband. The two cases that the transition rate matrix (TRM) of semi-Markov process is completely or partially known are respectively discussed. As a result, several sufficient conditions which can ensure to achieve exponential MSC are respectively deduced by means of stochastic Lyapunov theory and linear matrix inequality (LMI) theory. At last, two simulation tests corresponding to completely and partially known TRMs are designed to verify the correctness of the obtained conclusions.

Distributed adaptive consensus for nonlinear multi-agent systems under event-triggered communication using a polynomial approach

This article investigates the adaptive consensus tracking control of nonlinear multi-agent systems (MASs) under event-triggered communication (ETC). The discontinuity of triggered signals poses challenges to the design of backstepping controllers to require continuous state information. To solve this problem, a univariate polynomial approach is proposed which replaces the discontinuous parts in the triggered signal with a smooth function. The approach does not rely on the continuous information to an agent about the state of its neighbors. Then, by backstepping recursion and using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear function, the distributed adaptive consensus controller is designed. It is shown that all signals are uniformly bounded and the synchronization errors converge to a neighborhood of origin. The simulation results corroborate the effectiveness of the approach.

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs.

This paper investigates achieving leader-following consensus in a class of multi-agent systems with nonlinear dynamics. Initially, it introduces a dynamic event-triggered strategy designed to effectively alleviate the strain on the system's communication resources. Subsequently, a distributed control strategy is proposed and implemented in the nonlinear leader-follower system using the dynamic event-triggered mechanism, aiming to ensure synchronization across all nodes at an exponential convergence speed. Thirdly, the research shows that under the dynamic event-triggered strategy the minimum event interval of any two consecutive triggers guarantees the elimination of Zeno behavior. Lastly, the validity of the calculation results is verified by a simulation example.

Output feedback consensus for input-delayed nonlinear multi-agent systems via event-triggered communication

This paper designs two periodic event-triggered control protocols to achieve the output feedback leaderless consensus for nonlinear multi-agent systems with uniform input delays, and leader-follower consensus with nonuniform input delays. The involved system permits uncertainties in input delays and inherent nonlinearities, which brings the challenge on the design of observers. To overcome this challenge, a compensator is constructed independent of nonlinearities and input delays for every agent. To guarantee the event-triggered communication among different agents, two new periodic triggering mechanisms are designed based on the discrete output information. Moreover, these triggering mechanisms naturally avoid constantly monitoring and the Zeno behavior since triggering conditions are only checked on sampling points. Specifically, the designed event-triggered output feedback control protocols achieve convergence not boundedness of consensus errors. Finally, two examples are listed to demonstrate the validity of the two event-triggered strategies.

Aperiodically Intermittent Event-Triggered Optimal Average Consensus for Nonlinear Multi-Agent Systems.

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

Neural-Network-Based Predefined-Time Adaptive Consensus in Nonlinear Multi-Agent Systems With Switching Topologies.

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

Event-Based Robust Optimal Consensus Control for Nonlinear Multiagent System With Local Adaptive Dynamic Programming.

This article investigates the robust optimal consensus for nonlinear multiagent systems (MASs) through the local adaptive dynamic programming (ADP) approach and the event-triggered control method. Due to the nonlinearities in dynamics, the first part defines a novel measurement error to construct a distributed integral sliding-mode controller, and the consensus errors can approximately converge to the origin in a fixed time. Then, a modified cost function with augmented control is proposed to deal with the unmatched disturbances for the event-based optimal consensus controller. Specifically, a single network local ADP structure with novel concurrent learning is presented to approximate the optimal consensus policies, which guarantees the robustness of the MASs and the uniform ultimate boundedness (UUB) of the neural network (NN) weights' estimation error and relaxes the requirement of initial admissible control. Finally, an illustrative simulation verifies the effectiveness of the method.