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Summary: This article investigates the issue of orbit coordination control for a class of multi‐spacecraft formation systems in presence of limited communication and external disturbance. To solve the limitation of communication sources, a dynamic event trigger (DET) mechanism is developed to reduce the communication frequency between the follower spacecrafts. Subsequently, we explore a robust DET mechanism‐based distributed self‐learning sliding mode control design, in which a variable learning intensity‐based iterative learning algorithm is designed to approximate and compensate space perturbation. This approach can guarantee an event triggering sequence without Zeno phenomenon and accurate coordination control for formation configuration simultaneously. Compared with the traditional event‐triggered control and other state‐of‐the‐art approaches, the distributed DET control scheme achieves higher control accuracy of formation configuration meanwhile requires less communication resource. Finally, a series of numerical simulations demonstrate the feasibility and superiority of the event triggered control method. [ABSTRACT FROM AUTHOR]

This paper studies the issue of learning radial basis function neural network (RBFNN)-based robust reconfigurable fault-tolerant configuration control for spacecraft formation flying (SFF) systems subject to thruster faults and space perturbations. To robustly reconstruct thruster faults, a novel learning RBFNN estimator is innovatively explored, in which the P-type iterative learning algorithm is utilized to online update the weight matrix of the RBFNN model and the H∞ control technique is adopted to attenuate the effect of space perturbations. Further, a learning RBFNN output-feedback fault-tolerant control (FTC) method is developed for spacecraft formation configuration maintenance with high accuracy, and the learning RBFNN algorithm is used to update and compensate the synthesized perturbation. Finally, a numerical example is simulated to verify the presented learning RBFNN-based spacecraft formation FTC approach is feasible and superior. [ABSTRACT FROM AUTHOR]

The issue of active attitude fault-tolerant stabilization control for spacecrafts subject to actuator faults, inertia uncertainty, and external disturbances is investigated in this paper. To robustly and accurately reconstruct actuator faults, a novel mixed learning observer (MLO) is explored by combining the iterative learning algorithm and the repetitive learning algorithm. Moreover, to guarantee robust spacecraft attitude fault-tolerant stabilization, by synthesizing the mixed learning algorithm with the sliding mode controller, a novel mixed learning sliding-mode controller (MLSMC) is designed based on the separation principle, in which the mixed learning algorithm is used to update composite disturbances online, including fault errors, inertia uncertainty, and external disturbances. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of our proposed spacecraft attitude fault-tolerant stabilization control approach. [ABSTRACT FROM AUTHOR]

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]

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 robust attitude synchronization issue in a multi-spacecraft formation system subjected to limited communication, space disturbances, modeling uncertainties, and actuator faults. To accommodate limited inter-spacecraft communication, a dynamic event-triggered mechanism is designed to reduce the communication trigger frequency by dynamically adjusting the trigger threshold. Moreover, an event-based distributed self-learning neural-network control (SLN2C) law is developed to guarantee robust attitude synchronization during multi-spacecraft formation. In the SLN2C scheme, a learning radial basis function neural network (RBFNN) model is proposed to online approximate and compensate for lumped disturbances, in which an iterative learning algorithm with a variable learning intensity is adopted to update the weight matrix of the RBFNN model. Compared with the traditional fixed learning intensity, a variable one can reduce initial oscillation and weaken the saturation response. Numerical simulations and comparisons are performed to illustrate the effectiveness and superiority of the proposed event-based spacecraft attitude synchronization control method. [ABSTRACT FROM AUTHOR]