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Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators

Zhi Feng, Guoqiang Hu

2021IEEE Transactions on Neural Networks and Learning Systems30 citationsDOI

Abstract

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

Topics & Concepts

ActuatorControl theory (sociology)KinematicsNonlinear systemComputer scienceConvergence (economics)Tracking (education)Task (project management)Multi-agent systemFault detection and isolationFault toleranceControl (management)Control engineeringDistributed computingEngineeringArtificial intelligencePedagogyEconomicsPhysicsClassical mechanicsEconomic growthPsychologyQuantum mechanicsSystems engineeringDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationUAV Applications and Optimization
Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators | Litcius