In this work, an $H_{\infty}$ performance fault recovery control problem for a team of multi-agent systems that is subject to actuator faults is studied. Our main objective is to design a distributed control reconfiguration strategy such that \textbf{a)} in absence of disturbances the state consensus errors either remain bounded or converge to zero asymptotically, \textbf{b)} in presence of actuator fault the output of the faulty system behaves exactly the same as that of the healthy system, and \textbf{c)} the specified $H_{\infty}$ performance bound is guaranteed to be minimized in presence of bounded energy disturbances. The gains of the reconfigured control laws are selected first by employing a geometric approach where a set of controllers guarantees that the output of the faulty agent imitates that of the healthy agent and the consensus achievement objectives are satisfied. Next, the remaining degrees of freedom in the selection of the control law gains are used to minimize the bound on a specified $H_{\infty}$ performance index. The effects of uncertainties and imperfections in the FDI module decision in correctly estimating the fault severity as well as delays in invoking the reconfigured control laws are investigated and a bound on the maximum tolerable estimation uncertainties and time delays are obtained. Our proposed distributed and cooperative control recovery approach is applied to a team of five autonomous underwater vehicles to demonstrate its capabilities and effectiveness in accomplishing the overall team requirements subject to various actuator faults, delays in invoking the recovery control, fault estimation and isolation imperfections and unreliabilities under different control recovery scenarios.
Fault-Tolerant Formation Tracking of Heterogeneous Multi-Agent Systems with Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators
A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
Autonomous Industrial Systems Laboratory, Imperial College London（帝国理工学院自主工业系统实验室）；Institute of Automation Technology, Helmut Schmidt University（赫尔穆特·施密特大学自动化技术研究所）
Fault-Tolerant Temperature Control of HRSG Superheaters: Stability Analysis Under Valve Leakage Using Physics-Informed Neural Networks
AmirKabir University of Technology (Tehran Polythechnique)（阿米尔卡比尔理工大学（德黑兰理工学院））；K.N. Toosi University of Technology（K.N.托西理工大学）；Mapna Electric & Control Engineering & Manufacturing (MECO)（马普纳电气与控制工程制造公司）
Fault-Tolerant MARL for CAVs under Observation Perturbations for Highway On-Ramp Merging
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（北京信息科学与技术国家研究中心（BNRist），清华大学）
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
Airbus Defence and Space GmbH（阿依斯堡防务与航天有限公司）；Department of Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology（航天系统工程系，航空航天工程学院，代尔夫特理工大学）
