Distributed Secure Consensus Tracking of Multiagent Systems Under Hybrid Cyberattacks: An Event-Triggered Neuroadaptive Approach

This paper addresses the event-triggered consensus tracking for general linear multi-agent systems with nonlinear uncertainty dynamics under directed communication topology. The proposed event-triggered consensus strategy relaxes the assumptions in existing works that the control gain and local inputs must be continuous. With the consensus tracking condition being satisfied, the cooperative control law with bounded parameter is developed based on the intermittent samples. It shows that if the sampling condition, in terms of LMIs, is satisfied, the resulting closed-loop general linear multi-agent uncertain systems will achieve the consensus. Simulation results are provided to verify the proposed approach.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

This paper investigates the fixed-time-synchronized consensus tracking problem for multi-agent systems with undirected topology. A nonlinear distributed control law for first-order multi-agent systems is proposed based on norm-normalized sign function, with which all the elements of all the agents' states track the leader's state at the same time and in fixed time. Then, the corresponding results are extended to second-order multi-agent systems by adopting the terminal sliding mode control method. Finally, numerical simulations are performed to verify the theoretical results in this paper.

In this paper, the consensus tracking problem for nonlinear multi-agent systems subjected to the output constraint, data loss and switching topologies is considered. Firstly, a consensus term is defined based on the communication information in present of the nonlinear output saturation and time-varying switching topologies. Since the random data loss is taken into consideration, the consensus term is redesigned by an introducing stochastic variable, which obeys the Bernoulli sequence with known probability. Then, a novel distributed ILC algorithm is designed by using the incomplete communication data of agents, which is more universal than the results without considered nonlinear constraint and random factors simultaneously. Through the contraction mapping method, an obtained convergence condition can also guarantee the asymptotic convergence of the agent along the iteration axis under nonlinear saturation factor and random factors. It is verified that the proposed algorithm can handle more complex situations in the consensus control of multi-agent systems. Simulation examples are further provided to verify the effectiveness of the proposed algorithm.

In the technical note, consensus tracking is studied for multi-agents system (MAS) with state time-delays. Every agent of the MAS has homogeneous dynamic function, which is described by a nonlinearly parameterized functions with unknown state time-delays. Moreover, it is assumed that the virtual leader (regarded as the desired trajectory) can be accessible to at least one agent. Based on several necessary assumptions, a distributed adaptive ILC is designed. In order to prove the convergence of tracking error, a composite energy function is designed. The result of convergence analysis shows that the designed control strategy can guarantee the agents track virtual leader along the iteration axis. A numerical simulation is provided to illustrate the effectiveness of the adaptive iterative learning controller.

A distributed FDI cyber-attack detection in discrete-time nonlinear multi-agent systems using neural networks

This paper investigates the consensus tracking problem for the discrete multi-agent system (DMAS) with time-delay. By using iterative learning control (ILC) method and convergence in discrete system, the convergence properties for total system can be analyzed based on the time-domain analysis, frequency-domain analysis and matrix theory. The result shows that the proposed methods depend on each agent's self-delay time, the weights of the edges to each agent's neighbors, and the interconnection topology of the network. The iterative number of learning control relies on the sampling period and self-delay time for the discrete multi-agent system. Finally, numerical examples are presented to demonstrate the theoretical results.

Asymptotic bounded consensus tracking of multi-agent systems with a bounded-acceleration target via sampled-data control

This paper deals with the finite-time and fixed-time bipartite consensus tracking (FFBCT) problems for multi-agent systems (MASs) , in which both cooperative and competition exist. Using Lyapunov stability methods, distributed nonlinear protocols are provided to ensure all agents reach agreement. Its absolute value is the same but it might be different in direction. Besides, the second protocol can make the system reach bipartite consensus in fixed time regardless of the initial state of agents. Moreover, boundaries for settling times are estimated. Some numerical examples are finally presented to prove the validity of our designs.

This paper is concerned with the problem of bipartite consensus tracking for multi-agent systems with both cooperative and antagonistic interactions. These interactions among vertices in a signed digraph are first investigated with the signs of the weighted products of associated paths in their augmented undirected graph. To improve the convergence rate, a finite-time protocol and a fixed-time protocol are developed to solve the bipartite consensus tracking problem with a finite-time and a settling time independent of any initial states, respectively. Moreover, an M-matrix based method is well proposed to estimate all parameters in the constructed Lyapunov function, therefore, the upper bounds of the settling times for finite-time and fixed-time cases can be analytically determined. Finally, numerical examples are provided to demonstrate the effectiveness of proposed methods.

This brief considers the leader-following consensus (consensus tracking) problem of multi-agent systems (MAS) with event-triggered communication. A novel hybrid event-triggering mechanism is proposed to reduce the burden of network transmission, and it is ensured that Zeno behavior does not occur. Besides, it is required that the control protocol is only updated at agent’s own triggering instant. Compared with the existing works, the main contribution is that the minimum inter-event time (MIET) can be pre-specified, which is important for physical implementation, and the control update frequency is also reduced. An example is given to verify the correctness of the proposed hybrid event-triggered control strategy.

In this article, a novel cooperative resilient control method is proposed to solve the distributed resilient control issue of frequency restoration, fair active power sharing, and state-of-charge (SoC) balancing in AC microgrids (MGs) of energy storage systems (ESSs) under hybrid cyber attacks including false data injection (FDI) and denial-of-service (DoS) attacks. Different from the existing distributed resilient control methods on MGs under hybrid attacks, the developed method can guarantee the precise convergence of frequency restoration, fair active power sharing, and SoC balancing. Specifically, a novel local iterative attack observer is first designed to estimate FDI attacks accurately. Based on the mean of the iterative attack observation sequences, a distributed resilient controller is proposed to accurately compensate for the effects of hybrid attacks, and the stability of the closed-loop MG system is proven by the Lyapunov theory. To maximize the tolerance duration of DoS attacks, an optimization algorithm is proposed to select the controller parameters. Finally, the proposed attack resilient control method is implemented in MGs with four ESSs, and its effectiveness under hybrid attacks is validated on the real-time simulator OPAL-RT.

This paper proposes a resilient output feedback tube-based model predictive control (MPC) approach for constrained cyber-physical systems (CPSs) to handle the impact of stochastic hybrid attacks, where the hybrid attacks include false data injection (FDI) attacks and denial-of-service (DoS) attacks that occur in the sensor-controller (S-C) and controller-actuator (C-A) channels, respectively. The anomalous behavior of the attacker is revealed by the designed attack detector and comparator, which provide alert signals that guide the primary and auxiliary controllers to collaboratively generate control inputs as well as a nominal trajectory. The tolerable attack duration is determined by using the concept of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula>-step robust positive invariant (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula>-RPI) set, which limits the size of the deviation between the observer and the nominal trajectory under the hybrid attacks. Robust constraint satisfaction and robust asymptotic stability are ensured by restricting the state of the system to a tube centered on a nominal trajectory that converges gradually to the origin, and theoretical guarantees are provided. Finally, the effectiveness of the designed algorithm is validated through a supply chain model, which includes comparisons with an inelastic scheme.

This paper studies the consensus tracking problem of a class of multi-agent systems where the dynamics of the leader and the followers are described respectively by integer and fractional order differential equations. It is shown that leader-following consensus tracking can be ensured if some carefully selected followers are informed and the relative position-based protocols are appropriately designed. A necessary and sufficient condition for the leader-following consensus in multi-agent systems is first proposed. It is found that, the real and imaginary parts of the eigenvalues of the augmented Laplacian matrix of the topology play important roles in achieving consensus. Finally, some numerical simulations are given to verify the effectiveness of the theoretical results.

This paper investigates the problem of distributed secure consensus tracking for leader-following multi-agent systems under the presence of sparse data anomalies in the networks. An extra auxiliary function is designed and employed to formulate the interaction gains to mitigate the impact of such unknown sparse anomalies. Different from conventional anomaly detection and isolation methods, the introduction of the auxiliary function can alleviate the wasteful expansion of global sparse anomaly detectors and facilitate non-smooth control. Consequently, we formulate a new design framework for secure consensus protocols, enabling all followers to realize asymptotic and even finite-/fixed-time tracking to the leader. Finally, some simulation examples are presented to illustrate the effectiveness of the theoretical results.