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This paper investigates the attitude fault-tolerant stabilization problem for a spacecraft subjected to its actuator effectiveness loss, inertia uncertainties and space disturbances. A novel Iterative Learning Sliding Mode Observer (ILSMO) is proposed to reconstruct the actuator effectiveness factors robustly and accurately by combining the P-type iterative learning algorithm with the sliding mode approach. Based on the reconstructed fault signals, an Iterative Learning Sliding Mode Controller (ILSMC) is designed to guarantee the closed-loop spacecraft attitude fault-tolerant stabilization by compensating for its lumped disturbance. The ILSMO and ILSMC stabilities are guaranteed using the Lyapunov direct approach, respectively. Finally, the numerical simulation results show that the proposed ILSMO-ILSMC-based spacecraft attitude fault-tolerant stabilization method is effective and superior. [ABSTRACT FROM AUTHOR]

The issue of robust actuator fault reconstruction for a class of Takagi‐Sugeno (T‐S) fuzzy systems with actuator fault and unknown input via a novel Synthesized Learning and Sliding‐Mode Observer (SLSMO) is investigated in this paper. Through a coordinate transformation technique, the considered T‐S fuzzy system is decomposed into two separate subsystems: Subsystem 1 affected only by actuator fault and Subsystem 2 affected by unknown input and actuator fault. In the SLSMO methodology, for Subsystem 1, a new reduced‐order Fuzzy Learning Observer (FLO) is explored to accurately reconstruct actuator fault, while a reduced‐order Fuzzy Sliding Mode Observer (FSMO) is employed for Subsystem 2 such that it has strong robustness against actuator fault and unknown input. Stability and convergence of the fuzzy SLSMO are explicitly proved using Lyapunov's indirect method. The design issue of the reduced‐order FLO and of the reduced‐order FSMO can be uniformly formulated in terms of some Linear Matrix Inequalities (LMIs) that can be directly solved using LMI optimization technique. In addition, a new full‐order FLO is suggested for actuator fault reconstruction in a class of T‐S fuzzy system without unknown input. At the end, a numerical example is applied to verify the effectiveness and superiority of the proposed approaches. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to accomplish robust actuator fault isolation and identification for microsatellite attitude control systems (ACSs) subject to a series of space disturbance torques and gyro drifts. Design/methodology/approach: For the satellite attitude dynamics with Lipschitz constraint, a multi-objective nonlinear unknown input observer (NUIO) is explored to accomplish robust actuator fault isolation based on a synthesis of Hinf techniques and regional pole assignment technique. Subsequently, a novel disturbance-decoupling learning observer (D2LO) is proposed to identify the isolated actuator fault accurately. Additionally, the design of the NUIO and the D2LO are reformulated into convex optimization problems involving linear matrix inequalities (LMIs), which can be readily solved using standard LMI tools. Findings: The simulation studies on a microsatellite example are performed to prove the effectiveness and applicability of the proposed robust actuator fault isolation and identification methodologies. Practical implications: This research includes implications for the enhancement of reliability and safety of on-orbit microsatellites. Originality/value: This study proposes novel NUIO-based robust fault isolation and D2LO-based robust fault identification methodologies for spacecraft ACSs subject to a series of space disturbance torques and gyro drifts. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper addresses the problem of learning observer (LO)-based fault reconstruction in continuous-time systems. An LO is constructed such that simultaneous reconstruction of system states and actuator faults can be guaranteed. Then, existence conditions of LOs are first introduced to verify whether or not the LOs for fault reconstruction exist. An effective LO-design algorithm is explored such that observer gain matrices can be conveniently solved using linear matrix inequality technique. An extension to sensor-fault reconstruction is also investigated. Finally, two simulation examples are employed to demonstrate the effectiveness of the proposed LO-based fault-reconstructing approaches. [ABSTRACT FROM AUTHOR]

This paper addresses the problems of observer-based fault reconstruction and fault-tolerant control for Takagi–Sugeno fuzzy descriptor systems subject to time delays and external disturbances. A novel fuzzy descriptor learning observer is constructed to achieve simultaneous reconstruction of system states and actuator faults. Sufficient conditions for the existence of the proposed observer are explicitly provided. Utilizing the reconstructed fault information, a reconfigurable fuzzy fault-tolerant controller based on the separation property is designed to compensate for the impact of actuator faults on system performance by stabilizing the closed-loop system. In addition, the design of the fault reconstruction observer and the fault-tolerant controller is formulated in terms of linear matrix inequalities that can be conveniently solved using convex optimization techniques. Finally, simulation results on a truck–trailer system are presented to verify the effectiveness of the proposed approaches. [ABSTRACT FROM PUBLISHER]

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]