Adaptive inverse optimal consensus control for uncertain high-order multiagent systems with actuator and sensor failures

This paper addresses a neuroadaptive inverse optimal consensus problem of uncertain nonlinear multiagent systems (MASs) subject to actuator and sensor faults simultaneously. Unlike traditional adaptive dynamic programming methods, the proposed control mechanism minimizes a loss function without solv...

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Bibliographic Details
Published inInformation sciences Vol. 605; pp. 119 - 135
Main Authors Huang, Chengjie, Xie, Shengli, Liu, Zhi, Chen, C.L. Philip, Zhang, Yun
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.08.2022
Subjects
Actuator and sensor faults
Adaptive consensus
Inverse optimal control
Multiagent systems
Multiagent systems
Adaptive consensus
Inverse optimal control
Actuator and sensor faults
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Summary:This paper addresses a neuroadaptive inverse optimal consensus problem of uncertain nonlinear multiagent systems (MASs) subject to actuator and sensor faults simultaneously. Unlike traditional adaptive dynamic programming methods, the proposed control mechanism minimizes a loss function without solving the Hamilton-Jacobi-Bellman equation, which simplifies the computational workload. In addition, a compensation strategy for actuator and sensor faults is considered and a novel fault-tolerant adaptive inverse optimal protocol incorporating the Lyapunov design is constructed. It is demonstrated that the system is input-to-state stabilizable (ISS) under the designed inverse optimal controller and the tracking errors of the MASs can converge to a predefined range. A simulation example is presented to illustrate the effectiveness of the control design.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.05.021