故障下随机多智能体系统的混合无源/H∞容错一致性

本文研究了在有向拓扑结构下, 马尔可夫跳变故障多智能体系统的混合无源/H∞容错一致性问题. 首先, 针对系统中的执行器偏移故障, 基于增广矩阵方法设计故障观测器以估计未知的偏移故障. 进而, 利用所估故障信息, 基于混合无源/H∞性能指标, 利用代数图论、自由权矩阵、增广矩阵和线性矩阵不等式等方法, 设计了相应的容错控制器, 实现了故障情况下多智能体系统的容错一致性. 最后通过数值仿真验证了该方法的有效性.     

带有执行器故障的网络控制系统的自适应容错H∞控制

针对带有执行器故障的网络控制系统, 提出了一种自适应容错控制方法. 首先基于最近提出的一种新的网络诱导时滞模型, 设计了状态反馈形式的自适应容错控制器. 然后以线性矩阵不等式的形式给出了控制器存在的充分条件. 该条件不仅保证了系统在执行器故障和正常情形下均能达到稳定, 而且使得其H∞性能最优. 最后通过一个数值例子证明了所提方法的有效性.     

基于降阶卡尔曼滤波器的水下机器人滑模容错控制

针对水下机器人执行器时变、非线性故障,提出一种基于降阶卡尔曼滤波器的故障估计和滑模容错控制方法.用降阶卡尔曼滤波器估计水下机器人故障解耦子系统的状态,受故障的影响,子系统状态可测.由估计的状态和测量的状态可进一步得到水下机器人执行器的故障信息.滑模容错控制器根据所估计的执行器故障调整控制器的输出以实现容错控制.仿真结果验证了所提出的故障辨识与容错控制算法的有效性.     

虚假数据注入攻击下多无人机韧性容错协同控制

针对同时具有虚假数据注入(false data injection, FDI)攻击与执行器故障下的多无人系统编队协同跟踪问题,提出了一种基于FDI攻击检测机制与故障观测器的韧性容错协同控制新方法。首先,以二阶非线性固定翼无人机模型作为多无人系统研究对象;其次,构建了带有概率约束的贝叶斯概率检测模型对FDI攻击进行检测,作为韧性容错协同控制器的辅助系统;之后,设计了包含执行器故障补偿的韧性容错协同跟踪控制器,并利用Lyapunov稳定性理论证明系统的渐进稳定;最后,通过仿真验证了设计的控制器针对FDI攻击与执行器故障的安全性与可靠性以及编队跟踪能力。     

基于中间观测器的异构多智能体系统分布式故障估计

针对由无人机和无人车组成的异构多智能体系统,提出一种新型的基于中间观测器的分布式故障估计方法,可以实现对节点及其邻居执行器故障和系统状态的同时估计.首先,考虑到无人机在XOY平面与在OZ轴方向的运动相对独立,异构多智能体系统可以划分为由无人机和无人车组成的位置子系统的XOY平面以及无人机位置子系统的OZ轴;然后,设计基于中间变量的分布式故障估计观测器,不仅能同时估计出选定的智能体自身与其邻居的执行器故障和状态,也能克服观测器匹配条件的限制,并基于$H_\infty$性能指标求解观测器增益;最后,通过仿真实验验证所提出方法的可行性与有效性.     

不确定非线性多智能体系统的分布式容错协同控制

针对一类存在未知非线性的多智能体系统,研究具有执行器故障的“领导-跟随”协同控制问题。利用模糊逻辑系统逼近系统的未知非线性,通过设计故障估计器辨识系统的故障。在“跟随者”之间的通信网络为单向连通的情况下,提出分布式模糊容错协同控制器的设计方案,实现“跟随者”的状态跟踪“领导者”的状态。基于Lyapunov稳定性理论,证明系统的跟踪误差一致最终有界。仿真结果验证了所提出设计方法的有效性。     

模型不确定多智能体系统的鲁棒H∞容错一致性控制

针对具有模型不确定性的多智能体系统,考虑到任意智能体可能发生的执行器故障以及受到外界能量有限干扰,研究了鲁棒H_∞容错一致性控制的设计问题。首先,引入鲁棒二次稳定法处理多智能体系统建模中存在的模型不确定性;其次,利用LMI技术及Lyapunov稳定性理论等,对于任意智能体可能发生的执行器故障,设计了鲁棒H_∞被动容错控制器,使得模型不确定多智能体系统存在外界能量有限干扰时,在所设计的分布式控制协议下位置与速度状态趋于一致,且具有一定的扰动抑制性能。最后,通过MATLAB仿真验证了所提方法的有效性和可行性。     

考虑多传感器故障的可重构机械臂主动取代分散容错控制

针对可重构机械臂系统位置传感器和速度传感器多故障, 提出一种主动取代分散容错控制方法. 基于可重构机械臂的模块化属性, 设计正常工作模式下的分散神经网络控制器. 利用微分同胚原理将子系统结构进行非线性变换, 将传感器故障转化成伪执行器故障, 设计分散滑模观测器以对多传感器故障进行实时检测, 并利用其输出信号取代故障传感器信号, 实现了多传感器故障情形下可重构机械臂的主动容错控制. 仿真结果表明了所设计的容错控制方法的有效性.     

基于径向基函数神经网络故障观测器的风能转换系统容错控制器设计

针对风能转换系统执行器部分失效故障,提出了一种新型的主动容错控制策略.应用径向基函数(radial basis function,RBF)自适应神经网络,根据系统状态观测值对执行器故障进行在线重构,基于该重构故障,设计滑模容错控制器切换增益,实现风能转换系统故障诊断与容错控制律在线整定,并进行稳定性证明.仿真结果表明,执行器发生故障时系统的功率系数和叶尖速比均能保持在最优值,从而实现额定风速以下的最大风能捕获.     

多输入多输出最小相位系统的执行器故障自适应容错控制

针对一类具有执行器卡死或/和变执行器故障的多输入多输出(MIMO)非线性最小相位系统提出了自适应容错跟踪控制方案.结合系统特征对系统执行器进行分类,用神经网络逼近执行器未知故障函数,采用模型参考自适应容错控制方法设计控制律.所设计的控制律不仅保证闭环系统稳定,而且跟踪误差一致最终有界.仿真结果表明了所提出方法的有效性.     

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control (FTCC) problem of nonlinear multi-agent systems (MASs) under a directed network topology. The proposed control framework which is independent on the global information about the communication topology consists of two layers. Different from most existing distributed fault-tolerant control (FTC) protocols where the fault in one agent may propagate over network, the developed control method can eliminate the phenomenon of fault propagation. Based on the hierarchical control strategy, the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer. Finally, simulation results are given to demonstrate the effectiveness of the proposed control protocol.      

面向网络攻击下弹性均值趋同的智能体群组编队

本文研究通信范围有限的智能体群组编队问题, 探索网络攻击下多智能体系统弹性均值趋同控制策略. 现 有的工作表明, 多智能体系统可通过维持一个所谓r–鲁棒的通信网络, 实现分布式弹性趋同控制器的设计. 然而, 传 统的方法中只有当单个智能体的通信范围足够大时, 才能使网络满足r–鲁棒这一特定条件. 本文利用智能体可移动 的特性放宽了对通信范围的要求, 通过小组化和模块化的设计思想以及相应的编队策略, 让智能体沿预设轨道做周 期性运动, 从而达成具有r–鲁棒的通信网络, 并基于该通信网络提出了一种分布式弹性均值趋同控制方法. 此外, 分 析给出了单模块和多模块情况下多智能体网络达成r–鲁棒的所需条件. 最后在仿真实例中, 实现了网络攻击下系统 状态的均值趋同, 实验结果验证了群组编队和均值趋同控制方法的有效性.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

Physical Safety and Cyber Security Analysis of Multi-Agent Systems: A Survey of Recent Advances

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making capabilities.Nowadays,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation systems.Control of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation performance.However,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of MASs.This paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber attacks.Finally,the paper concludes with some potential future research topics on the security issues of MASs.     

Event-triggered containment control for multi-agent systems under hybrid cyber attacks

This paper studies event-triggered containment control problem of multi-agent systems (MASs) under deception attacks and denial-of-service (DoS) attacks. First, to save limited network resources, an event-triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing event-triggered mechanisms, the negative influences of deception attacks and DoS attacks are considered in the proposed triggering function. The communication frequencies between agents are reduced. Then, based on the proposed event-triggered mechanism, a corresponding control protocol is proposed to ensure that the followers will converge to the convex hull formed by the leaders under deception attacks and DoS attacks. Compared with the previous researches about containment control, in addition to hybrid cyber attacks being considered, the nonlinear functions related to the states of the agents are applied to describe the deception attack signals in the MAS. By orthogonal transformation of deception attack signals, the containment control problem under deception attacks and DoS attacks is reformulated as a stability problem. Then, the sufficient conditions on containment control can be obtained. Finally, a set of simulation example is used to verify the effectiveness of the proposed method.     

Observer-based optimal fault-tolerant control for offshore platforms

This paper is concerned with an observer-based optimal fault-tolerant control for an offshore steel jacket platform. The dynamic characteristics of the actuator faults under consideration are formulated by an exogenous system. Based on a dynamic fault observer designed, a feedforward and feedback optimal fault-tolerant controller is developed to improve the reliability of the offshore platform. The controller can be obtained by solving an algebraic Riccati equation and Sylvester equations, respectively. It is shown through simulation results that the proposed control scheme is effective to guarantee the reliability of the offshore platform with the actuator faults. The vibration amplitudes of the displacement, the velocity of the offshore platform and the required control force under the proposed fault-tolerant controller can be reduced significantly.     

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader’s input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.      

Distributed event-triggered optimal bipartite consensus control for multiagent systems with input delay via reinforcement learning method

In this article, an optimal bipartite consensus control (OBCC) scheme is proposed for heterogeneous multiagent systems (MASs) with input delay by reinforcement learning (RL) algorithm. A directed signed graph is established to construct MASs with competitive and cooperative relationships, and model reduction method is developed to tackle input delay problem. Then, based on the Hamilton–Jacobi–Bellman (HJB) equation, policy iteration method is utilized to design the bipartite consensus controller, which consists of value function and optimal controller. Further, a distributed event-triggered function is proposed to increase control efficiency, which only requires information from its own agent and neighboring agents. Based on the input-to-state stability (ISS) function and Lyapunov function, sufficient conditions for the stability of MASs can be derived. Apart from that, RL algorithm is employed to solve the event-triggered OBCC problem in MASs, where critic neural networks (NNs) and actor NNs estimate value function and control policy, respectively. Finally, simulation results are given to validate the feasibility and efficiency of the proposed algorithm.     

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks over an undirected graph. A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements. Meanwhile, this control scheme can also provide more reasonable control signals when DoS attacks occur. To save network resources, an adaptive memory event-triggered mechanism (AMETM) is also proposed and Zeno behavior is excluded. It is worth mentioning that the AMETM’s updates do not require global information. Then, the observer and controller gains are obtained by using the linear matrix inequality (LMI) technique. Finally, simulation examples show the effectiveness of the proposed control scheme.      

自适应事件触发通信机制下机理解析与数据驱动融合的ICPS双重安全控制

针对存在拒绝服务(DoS)攻击与执行器故障的工业信息物理融合系统(ICPS),将机理解析与数据驱动方法相结合,在新型自适应事件触发通信机制下,研究双重安全控制问题.首先,设计自适应事件触发机制,能够触发参数随系统行为动态自适应变化,节约更多网络通信资源;其次,基于系统最大允许时延建立攻击检测机制,可以有效区分大、小能量DoS攻击;再次,基于极限学习机算法(ELM)建立时序预测模型,用于大能量DoS攻击时重构修正控制量,以主动容侵攻击的影响,并给出与小能量攻击时机理解析的弹性被动容侵来提升系统对攻击的防御能力;然后,借助T-S模糊理论、时滞系统理论、新型Bessel-Legendre不等式等,推证得到系统鲁棒观测器及双重安全控制器的解析求解方法,使双重安全控制与通讯性能得到折衷协同提升;最后,通过实例仿真验证所提出方法的有效性.