Abstract

This article studies the problem of cooperative fault-tolerant output regulation of leader-follower multi-agent systems with sensor faults. To compensate for the faults existing in the followers, distributed observers based on relative output estimation errors are firstly designed. Then an adaptive fault-tolerant output regulation framework is built by solving the regulator equation. It is shown that stability of the closed-loop system can be ensured and that all tracking errors will converge to zero under the designed fault-tolerant controller. Finally, simulation results demonstrate the effectiveness of the proposed control law.

Highlights

  • In recent years, cooperative control for multi-agent systems (MASs) has become a hot spot in the field of control, which has been applied in multi-sensor networks [1], satellite networks [2] and cooperative vehicle infrastructure systems [3], etc

  • Output regulation theory [4] is used to solve several classes of consensus problems of MASs, which are called as the cooperative output regulation problems (CORPs)

  • Based on the above analysis, we have provided control law with gain matrices and control gains from the regulator equation, the following theorem is given: Theorem 2: Based on Assumptions 3 to 5, select K1N such that A N + B N K1N is stable and K2N as (37), the fault-tolerant output regulation problem (FTORP) can be solved by the controller (30)

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Summary

INTRODUCTION

Cooperative control for multi-agent systems (MASs) has become a hot spot in the field of control, which has been applied in multi-sensor networks [1], satellite networks [2] and cooperative vehicle infrastructure systems [3], etc. Zhang et al [16] studied the similar FTORP of linear MASs with an undirected topology and multiple leaders, and adaptive observers were designed to estimate states and faults of followers. We are ready to present the following observer of the fault-tolerant consensus problem It provides the estimation of the sensor faults for control law. J∈Ni where K1 ∈ Rm×n and K2 ∈ Rm×d are gain matrices to be designed, ηi(t) ∈ Rd is the estimation of v(t), μi(t) is a positive function representing adaptive gain, Ni are neighbors of the node i, aij is the element in Laplacian matrix LG of G, and ai0 represents the information transmission between the ith follower and the leader.

B N K1N 0
SIMULATION
CONCLUSION
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