Distributed Fault-Tolerant Consensus Tracking Control of Multi-Agent Systems Under Fixed and Switching Topologies

Abstract

This study investigates the distributed fault-tolerant consensus control problem of multi-agent systems subject to simultaneous actuator/sensor faults and channel noises in physical layer and hostile connectivity-mixed attacks in cyber layer. Actuator/sensor faults are remodeled into unified abrupt-type and incipient-type characteristics, and connectivity-mixed attacks are established with connectivity-maintained and connectivity-paralyzed topologies by a switching and nonoverlapping version. Normalization and estimationbased observer is devised to recollect unknown state and fault observations, and distributed anti-attack fault-tolerant consensus control is also developed to achieve the tolerance to faults, resilience to attacks and robustness to noises, respectively, with the novel incorporated sensor fault and output channel noise estimation as well as neighboring output information. Criteria of reaching leader-following consensus of multi-agent systems under cyber-physical threats are derived with attack frequency and activation rate technique. Effectiveness and improvements of the proposed fault-tolerant consensus algorithm are validated on two case studies: 1) multi-machine power system synchronization and 2) multi-aircraft system coordination .

Finite‐time adaptive consensus of stochastic multi‐agent systems with node‐based and edge‐based adaptive law design methods

“…In this section, we will propose a consensus protocol with node-based adaptive law design method and establish the global finite-time consensus criterion for SMASs (1). Denote the sgn a (𝜉) =…”

“…∑ N j=1 a ij sgn 0.5 (x i − x j ), ̇cij = a ij (x i − x j ) T (x i − x j ), i = 1, … , 4, ( F I G U R E 7 When noise intensity 𝜎 = 3, the edge-based finite-time adaptive consensus of nonlinear SMASs (17) under noise interference F I G U R E 8 Edge-based finite-time adaptive consensus of nonlinear SMASs (17) where c ij (0) ∈ [1,3] 17) under the consensus protocol (19) are shown in Figure 7, and the simulation of the finite-time consensus is shown in Figure 8.…”

“…Over the last few decades, MASs have become the frontiers and research hotspots in the intelligence control, and have been widely used in automation, artificial intelligence, unmanned driving, manufacturing, military and other fields, mainly because of its coordination, self-organization, robustness, reliability, and fault-tolerance. [1][2][3][4][5] Consensus is a fundamental control goal for formation control and cooperative control of MASs. Its main research is to design some effective protocols based on local information to guarantee that all states of the system agents converge to a same value.…”

“…In Section 3, we propose a consensus protocol with node-based adaptive law design method, and prove that the protocol can guarantee that the SMASs (1) achieve the finite-time consensus. In Section 4, we also propose a consensus protocol with edge-based adaptive law design method, and prove its effectiveness for SMASs (1). In Section 5, two simulation results are given to illustrate the effectiveness of the two consensus protocols for SMASs (1).…”

“…In Section 4, we also propose a consensus protocol with edge-based adaptive law design method, and prove its effectiveness for SMASs (1). In Section 5, two simulation results are given to illustrate the effectiveness of the two consensus protocols for SMASs (1). The conclusion is presented in Section 6.…”