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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]

This paper investigates the issues of iterative learning algorithm-based robust thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying systems subject to space perturbations. Motivated by sliding mode methodology, a novel iterative learning observer (ILO) was developed to robustly reconstruct the thruster faults. Based on the fault signals obtained from the ILO, a learning output–feedback fault-tolerant control (LOF2TC) approach was explored such that the closed-loop spacecraft formation configuration was accurately maintained in the presence of space perturbations and thruster faults. Numerical simulations were employed to demonstrate the effectiveness and superiority of the proposed ILO-based fault-reconstructing approach and LOF2TC-based configuration maintenance approach for spacecraft formation flying systems. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of integrated fault reconstruction and fault-tolerant control in linear systems subject to actuator faults via learning observers (LOs). A reconfigurable fault-tolerant controller is designed based on the constructed LO to compensate for the influence of actuator faults by stabilising the closed-loop system. An integrated design of the proposed LO and the fault-tolerant controller is explored such that their performance can be simultaneously considered and their coupling problem can be effectively solved. In addition, such an integrated design is formulated in terms of linear matrix inequalities (LMIs) that can be conveniently solved in a unified framework using LMI optimisation technique. At last, simulation studies on a micro-satellite attitude control system are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of observer-based fault reconstruction for Takagi–Sugeno fuzzy systems. Two types of fuzzy learning observers are constructed to achieve simultaneous reconstruction of system states and actuator faults. Stability and convergence of the proposed observers are proved using Lyapunov stability theory, and necessary conditions for the existence of the observers are further discussed. The design of fuzzy learning observers can be formulated in terms of a series of linear matrix inequalities that can be conveniently solved using convex optimisation technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-reconstructing approaches. [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]