Resilient and Robust Synchronization of Multiagent Systems Under Attacks on Sensors and Actuators

Induced attack with prescribed consensus trajectory against multiagent systems

This paper proposes a new induced attack strategy against multiagent systems from the perspective of the attacker. It is noted that the induced attack can drive multiagent systems as a whole to follow a specific trajectory prescribed by the attacker, which cannot be achieved by denial-of-service attacks or deception attacks. Firstly, the induced attack signal is produced by establishing an attack generation exosystem, whose dynamics can be regulated to generate the prescribed consensus trajectory. Then, by the local state information and the induced attack signal among partial agents, a new induced attack protocol is proposed, which consists of the nominal consensus term and the induced attack term. By constructing the projection of the induced attack signal onto the consensus subspace, an explicit expression of the prescribed consensus trajectory is determined, which describes the movement trajectory of the entire multiagent system under the induced attack. Meanwhile, the induced attack design criterion is proposed to determine the dynamics matrix of the attack generation exosystem via the robust H∞ scheme. Finally, the simulation example is performed to illustrate the effectiveness of theoretical results.

Sampled-data exponential consensus of multi-agent systems with Lipschitz nonlinearities

In this paper, the exponential consensus of leaderless and leader-following multi-agent systems with Lipschitz nonlinear dynamics is illustrated with aperiodic sampled-data control using a two-sided loop-based Lyapunov functional (LBLF). Firstly, applying input delay approach to reformulate the resulting sampled-data system as a continuous system with time-varying delay in the control input. A two-sided LBLF which captures the information on sampled-data pattern is constructed and the symmetry of the Laplacian matrix together with Newton-Leibniz formula have been employed to obtain reduced number of decision variables and decreased LMI dimensions for the exponential sampled-data consensus problem. Subsequently, an aperiodic sampled-data controller was designed to simplify and enhance stability conditions for computation and optimization purposes in the proposed approach. Finally, based on the controller design, simulation examples including the power system are proposed to illustrate the theoretical analysis, moreover, a larger sampled-data interval can be acquired by this method than other literature, thereby conserving bandwidth and reducing communication resources.

Distributed Adaptive Nash Equilibrium Solution for Differential Graphical Games

This article investigates differential graphical games for linear multiagent systems with a leader on fixed communication graphs. The objective is to make each agent synchronize to the leader and, meanwhile, optimize a performance index, which depends on the control policies of its own and its neighbors. To this end, a distributed adaptive Nash equilibrium solution is proposed for the differential graphical games. This solution, in contrast to the existing ones, is not only Nash but also fully distributed in the sense that each agent only uses local information of its own and its immediate neighbors without using any global information of the communication graph. Moreover, the asymptotic stability and global Nash equilibrium properties are analyzed for the proposed distributed adaptive Nash equilibrium solution. As an illustrative example, the differential graphical game solution is applied to the microgrid secondary control problem to achieve fully distributed voltage synchronization with optimized performance.

Sliding-Mode-Based Admissible Consensus Tracking of Nonlinear Singular Multiagent Systems Under Jointly Connected Topologies

The admissible consensus tracking problem of nonlinear singular multiagent systems (SMASs) with time-varying delay, uncertainties, and external disturbances under jointly connected topologies is investigated in this article. First, the sliding-mode control (SMC) is applied to effectively reduce the adverse effects of uncertainties and nonlinearities of systems. Then, by the combination of admissible analysis, the Cauchy convergence criterion, and SMC, the sufficient conditions for the admissible consensus tracking and disturbance rejection of SMASs under jointly connected topologies are provided. Furthermore, a distributed SMC law is designed such that the sliding-mode dynamics trajectories reach the sliding surface in finite time. Finally, the simulation results are utilized to indicate the effectiveness of the presented methods.

Secure predictor-based neural dynamic surface control of nonlinear cyber-physical systems against sensor and actuator attacks

This paper addresses a secure predictor-based neural dynamic surface control (SPNDSC) issue for a cyber-physical system in a nontriangular form suffering from both sensor and actuator deception attacks. To avoid the algebraic loop problem, only partial states are employed as input vectors of neural networks (NNs) for approximating unknown dynamics, and compensation terms are further developed to offset approximation errors from NNs. With introduction of nonlinear gain functions and attack compensators, adverse effects of an intelligent adversary are alleviated effectively. Furthermore, we present stability analysis and prove the ultimate boundedness of all signals in the closed-loop system. The effectiveness of the proposed control strategy is illustrated by two examples.

Adaptive Resilient Event-Triggered Control Design of Autonomous Vehicles With an Iterative Single Critic Learning Framework

This article investigates the adaptive resilient event-triggered control for rear-wheel-drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle's control during the running process. According to the kinematic equation of RWDA vehicles and the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design is considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.

Frequency domain analysis of resilient consensus in multi-agent systems subject to an integrity attack

Unmanned systems like autonomous surface vessels are being enhanced with the communication infrastructure to improve their reliability, efficiency and sustainability. Regardless of the significance advantages, their open communication network and connectivity renders these systems to a variety of cyber-attacks. This paper considers the resilient consensus problem of multi-agent systems (MASs) under an integrity attack. Unlike the existing works in the time domain, a resilient consensus controller is designed and analyzed for MASs in the frequency domain. To this end, analysis and modeling of MASs under the integrity attacks (MU-IA) are addressed in the frequency domain in which each agent is a linear continuous system with an input time delay. A resilient H_∞ controller is proposed to tackle the tampering of information due to an integrity attack. The proposed H_∞ controller is designed based on an internal stability method for performance tracking and robustness of the MU-IA. A significant strength of this scheme is that the current approach does not enforce any limit on the number of agents or neighboring agents under the integrity attacks. A quantitative tuning method is used to trade off the nominal performance and robustness of the MU-IA. Simulation results show the effectiveness of the proposed control strategy.