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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 Special Issue aims to highlight the state-of-the-art techniques that are used for advanced fault diagnosis and the fault-tolerant control technology of spacecraft, especially for intelligent fault diagnosis algorithm development and sliding mode fault-tolerant control. Therefore, alongside recent developments in various learning algorithms, advanced observer design, intelligent control, etc., the application of advanced fault diagnosis and FTC techniques to single complicated spacecrafts or distributed spacecraft systems (spacecraft formation flying system, spacecraft cluster, etc.) will be extensively investigated in this Special Issue. In order to complete the various tasks in advanced space missions, such as communication, navigation, and remote sensing, single complicated spacecraft and many distributed spacecraft systems have been launched into orbit. [Extracted from the article]

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]

With growing global concern for mainstreaming of environmental protection measures, the promotion of climate change and sustainable development policies, the international community, represented by international organizations, research institutions, and world‐wide businesses, has focused on green finance. Existing research on green finance in the context of environmental trends has been reviewed in terms of the products and determinants of green finance. This study aims to assess the impact of green finance on the decarbonization of economies in the context of a review of current and potential problems and prospects. Based on the examples of the People's Republic of China, the Russian Federation, and the United States of America, the study suggests that today green finance has not yet demonstrated a tangible effect in these countries. Based on the results of the study, it can be argued that in the three countries, the main tool of green finance in the field of decarbonization is green bonds; other types of green finance are used to a limited extent. The progress of the United States and China is not so obvious, but based on the overall involvement of relevant government agencies and businesses in the green bond market, it can be assumed that the results will be observed in the near future. [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 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]

This paper investigates the issue of robust relative orbit synchronization control for spacecraft cluster subjects to space perturbation. First, the relative orbit dynamics of the leader-follower spacecraft cluster is established. Combined with P-type learning algorithm and sliding mode control (SMC) strategy, a novel distributed learning SMC (LSMC) strategy with the consensus algorithm is explored such that all the follower spacecrafts converge to the target relative position and their velocities tend to the leader spacecraft. The distributed P-type learning algorithm is used to update and compensate space perturbation for all the follower spacecrafts. The proposed LSMC approach has the potential to achieve higher control accuracy than the traditional SMC one while it has the lower computation burden than the distributed adaptive SMC one. The stability analysis of the proposed distributed LSMC strategy is provided in detail. Finally, a numerical example is simulated to illustrate the effectiveness and superiority of the proposed distributed LSMC-based relative orbit synchronization control for spacecraft cluster. [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]

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]

Meloxicam (MLX) is a non-steroidal anti-inflammatory drug used to treat rheumatoid arthritis and osteoarthritis. However, its poor water solubility limits the dissolution process and influences absorption. In order to solve this problem and improve its bioavailability, we prepared it in nanocrystals with three different particle sizes to improve solubility and compare the differences between various particle sizes. The nanocrystal particle sizes were studied through dynamic light scattering (DLS) and laser scattering (LS). Transmission electron microscopy (TEM) was used to characterize the morphology of nanocrystals. The sizes of meloxicam-nanocrystals-A (MLX-NCs-A), meloxicam-nanocrystals-B (MLX-NCs-B), and meloxicam-nanocrystals-C (MLX-NCs-C) were 3.262 ± 0.016 μm, 460.2 ± 9.5 nm, and 204.9 ± 2.8 nm, respectively. Molecular simulation was used to explore the distribution and interaction energy of MLX molecules and stabilizer molecules in water. The results of differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) proved that the crystalline state did not change in the preparation process. Transport studies of the Caco-2 cell model indicated that the cumulative degree of transport would increase as the particle size decreased. Additionally, plasma concentration–time curves showed that the AUC0–∞ of MLX-NCs-C were 3.58- and 2.92-fold greater than those of MLX-NCs-A and MLX-NCs-B, respectively. These results indicate that preparing MLX in nanocrystals can effectively improve the bioavailability, and the particle size of nanocrystals is an important factor in transmission and absorption. [ABSTRACT FROM AUTHOR]