Resilient Distributed Control in the Presence of Misbehaving Agents in Networked Control Systems

Abstract

In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks.

Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation

“…It is clear that the controllability of the LSRM network in [31] without the IMC should first be satisfied, which is equivalent to the fact that I N ⊗ A − cL ⊗ BK and B ⊗ B consist of a controllable matrix pair. Unfortunately, the classic Kalman rank criteria of the linear system [31] are not able to illustrate the essential relationship between the controllability of the LSRM network Equation (15) and the controllability of the LSRM node (A, B) (termed as a physical system) together with the coordinated network (L, B) (termed as a cyber system) in Equation (15).…”

“…A controllability criteria of the LSRM network derived in [32] can be employed for revealing the controllability condition of the LSRM network Equation (15). Since controllability and observability are dual, Proposition 1 can be proven similarly.…”

“…A flocking algorithm combined with the consensus and attraction/repulsion function is implemented for the robotic fish leader-follower network to enable follower fish to track the leader fish to reach the desired destination [6]. The formation control of multiple mobile robots based leader-follower network is investigated by [14] and a reputation-based, distributed control algorithm is introduced for the leader-follower consensus network in the presence of misbehaving agents [15].…”

A Distributed Model Predictive Control Scheme with Robustness Against Noncompliant Controllers

“…are often exploited to disrupt its normal operation. Even though this is a significant concern in networked applications such as the smart grid [10] and consensus problems [11], few attempts have been made to deal with it in a DMPC framework. For example, heuristic defense mechanisms for dual decomposition DMPC are proposed in [12], [13] to minimize the effects of attacks of the price based coordination mechanism.…”

A Survey on Security and Privacy in Emerging Sensor Networks: From Viewpoint of Close-Loop

“…More sophisticated examples than the simple feedback system in Figure 1 include power networks, social networks, smart transportation systems, sensor networks, smart buildings, etc. [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. These systems are often distributed in nature and have a hierarchical structure.…”