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This study investigates the problem of reconstructing actuator faults for descriptor systems via a PD‐type learning observer. By synthesising the derivatives of the output estimation error into the P‐type learning law, a novel PD‐type learning observer is established to simultaneously reconstruct original system states and actuator faults. Stability analysis of the PD‐type learning observer is explicitly provided. A systematic design method is also suggested based on a linear matrix inequality technique. Further, a robust PD‐type learning observer is designed against process disturbances and measurement noises. At last, a simulation example is used to demonstrate the effectiveness of the proposed fault‐reconstructing method. [ABSTRACT FROM AUTHOR]

In this study, the problem of robust fault reconstruction in polytopic linear parameter‐varying systems, subject to loss of actuator effectiveness and external disturbances, is investigated using a learning observer (LO). A polytopic LO is proposed to achieve system state estimation and actuator fault reconstruction at the same time. The proposed LO is sensitive to incipient faults because no chattering is induced. Both constant and time‐varying faults can be accurately reconstructed and detected. Moreover, simultaneous fault reconstruction and detection can be performed using a single LO because the reconstructed fault signal can be employed as a fault alarm. The stability and convergence of polytopic LO and the uniformly ultimately boundedness of the dynamic error system is proved using Lyapunov stability theory and H∞ techniques. A systematic design of the LO is effectively solved using standard linear‐matrix‐inequality techniques. At last, simulation results on a satellite system for attitude control verify the effectiveness of the proposed fault‐reconstruction approach. [ABSTRACT FROM AUTHOR]