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In this paper, the simplified electromagnetic force/torque model and coupled orbit-attitude dynamics modeling in spacecraft electromagnetic docking are investigated, and an improved sliding mode control scheme based on planned trajectory is proposed. In this scenario, the docking two spacecraft are equipped with four energized solenoids with iron cores fixed in the body frame, and small-angle hypothesis is used to derive the simplified electromagnetic force/torque model, based on which the coupled orbit-attitude dynamics equation is established. With trajectory planning of relative orbit and attitude, where the tracking process of coupled orbit-attitude is divided into three successive parts with predefined time using three characteristic time instants, a sliding mode control strategy is proposed to solve the tracking problem. Simulation results illustrate the simplification rationality of electromagnetic force/torque model, and the good tracking performance of coupled orbit-attitude tracking controller at predefined time.

This paper studies the problem of attitude stabilization for a rigid spacecraft subject to external disturbances, time-varying inertia uncertainties, and input magnitude and rate constraints (MRCs). By analyzing the influence of inertia uncertainties, the reconstruction of lumped disturbances is accomplished to facilitate the controller design. Then, a disturbance observer is designed, based on which, a simple state feedback control strategy including estimation of lumped disturbances is proposed. By choosing new state variables using attitude information and estimation errors, an augmented plant is constructed. Using standard Lyapunov stability analysis, which shows that all states are uniformly ultimately bounded, sufficient conditions for the existence of the disturbance observer and controller are given based on linear matrix inequalities (LMIs). It is worth pointing out that the observer and controller gains are obtained simultaneously. It is shown that the control scheme developed is not only robust against external disturbances and unknown time-varying inertia uncertainties, but also able to steer the attitude control performance and estimation errors of lumped disturbances. Numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy.

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

This paper addresses the control problem of spacecraft electromagnetic docking in the presence of external disturbances, fault signals, elliptical eccentricity, and input magnitude and rate constraints (MRCs). More specifically, the dynamic model of translational motion in an elliptical orbit is derived first; by analyzing the influence of external disturbances, fault signals and elliptical eccentricity, a lumped disturbance is reconstructed to facilitate the controller design. Then, a state feedback control strategy based on disturbance observer, i.e. a disturbance observer-based controller (DOBC), is proposed, where the compensation of the lumped disturbance is considered. This controller thus has the nature of fault tolerance and robustness, and it requires no mass information on chaser or target spacecraft, no information on external disturbances, fault signals or even orbital elements except for semi-major axis. By choosing new state variables using the information on relative motion and estimation errors, an augmented plant is established. Using Lyapunov stability analysis, which shows that all states are uniformly ultimately bounded, sufficient conditions for the existence of the disturbance observer and controller subject to input MRCs are given based on linear matrix inequalities (LMIs). Numerical simulations are performed to demonstrate the validity and performance of the proposed strategy.

The observer-based fault tolerant attitude control problem for spacecraft is addressed, wherein the constraints of H∞ performance, quadratic stability, model parameter uncertainty, measurement errors, external disturbances, controller perturbation, and actuator fault and saturation are considered simultaneously. A novel fault estimation approach is proposed, where a stochastically intermediate variable is introduced. Based on the exactly available expectation of such a variable, a new kind of intermediate observer is proposed to estimate the attitude information and fault signals simultaneously. Compared with the traditional fault estimation methods, the approach proposed in this paper is less conservative for it requires neither equation constraints nor observer matching condition and can tolerate stochastic failure. To achieve fault tolerant and nonfragile control performance, the estimation of fault signals and explicit controller perturbation are also exploited in the controller design. Based on Lyapunov stability theory, sufficient conditions for the existence of the observer-based fault tolerant nonfragile H∞ controller are given based on linear matrix inequalities (LMIs) in terms of additive perturbation and multiplicative perturbation. Numerical simulations are performed to demonstrate the effectiveness of the proposed method and also illustrate the advantage over the existing state feedback control method in terms of great reduction in energy consumption.

Background Intracranial aneurysm is a common type of cerebrovascular disease with a risk of devastating subarachnoid hemorrhage if it is ruptured. Accurate computer-aided detection of aneurysms can help doctors improve the diagnostic accuracy, and it is very helpful in reducing the risk of subarachnoid hemorrhage. Aneurysms are detected in 2D or 3D images from different modalities. 3D images can provide more vascular information than 2D images, and it is more difficult to detect. The detection performance of 2D images is related to the angle of view; it may take several angles to determine the aneurysm. As the gold standard for the diagnosis of vascular diseases, the detection on digital subtraction angiography (DSA) has more clinical value than other modalities. In this study, we proposed an adaptive multiscale filter to detect intracranial aneurysms on 3D-DSA. Methods Adaptive aneurysm detection consists of three parts. The first part is a filter based on Hessian matrix eigenvalues, whose parameters are automatically obtained by Bayesian optimization. The second part is aneurysm extraction based on region growth and adaptive thresholding. The third part is the iterative detection strategy for multiple aneurysms. Results The proposed method was quantitatively evaluated on data sets of 145 patients. The results showed a detection precision of 94.6%, and a sensitivity of 96.4% with a false-positive rate of 6.2%. Among aneurysms smaller than 5 mm, 93.9% were found. Compared with aneurysm detection on 2D-DSA, automatic detection on 3D-DSA can effectively reduce the misdiagnosis rate and obtain more accurate detection results. Compared with other modalities detection, we also get similar or better detection performance. Conclusions The experimental results show that the proposed method is stable and reliable for aneurysm detection, which provides an option for doctors to accurately diagnose aneurysms.

Electromagnetic docking could enable autonomous spacecraft docking with no need for propellant consumption and without plume contamination. This paper addresses the robust electromagnetic docking problem for spacecraft in the presence of external disturbances, fault signals, unknown mass, elliptical eccentricity, measurement errors and input constraints. In this scenario, an intermediate observer is developed to estimate the relative motion information and the lumped disturbance resulting from these uncertainties. Based on this, an anti-disturbance controller is proposed, where the compensation of the lumped disturbance is considered. It is proved via Lyapunov analysis that the intermediate observer-based controller can achieve the objective of spacecraft electromagnetic docking with input constraints and in the presence of uncertainties. Finally, the observer-based controller is illustrated, in simulation, to demonstrate the effectiveness and improved performance compared with a disturbance observer-based controller.

A continuous globally stable control algorithm is presented to track angular velocity for spacecraft chaotic attitude motion affected by external disturbances using adaptive variable structure controller. Affected by some external disturbances, the spacecraft attitude dynamics system can generate many types of chaotic motion. Once it is required that a spacecraft with chaotic attitude motion should track the other spacecraft chaotic attitude plant to achieve angular velocity synchronization, the design of a robust tracking controller becomes necessary. The controller design is based on adaptive control theory and variable structure control theory, and adopts integral sliding surface and a single vector adjusted dynamically. Numerical simulations are performed to demonstrate the effectiveness and feasibility of the proposed adaptive variable structure controller.

A novel improved mixed H2/H∞ control technique combined with poles assignment theory is presented to achieve attitude stabilization and vibration suppression simultaneously for flexible spacecraft in this paper. The flexible spacecraft dynamics system is described and transformed into corresponding state space form. Based on linear matrix inequalities (LMIs) scheme and poles assignment theory, the improved mixed H2/H∞ controller does not restrict the equivalence of the two Lyapunov variables involved in H2 and H∞ performance, which can reduce conservatives compared with traditional mixed H2/H∞ controller. Moreover, it can eliminate the coupling of Lyapunov matrix variables and system matrices by introducing slack variable that provides additional degree of freedom. Several simulations are performed to demonstrate the effectiveness and feasibility of the proposed method in this paper.

This paper investigates the robust dynamic output feedback non-fragile control (RDOFNFC) strategy for spacecraft attitude stabilization problem with nonlinear perturbations. The spacecraft attitude dynamics model takes the actuator saturation limits, external disturbances, controller perturbation and model parameter uncertainty into account. To make spacecraft attitude system satisfy H ∞ performance and quadratic stability, with respect to the additive perturbation in initial state and multiplicative perturbation in decay phase, the corresponding RDOFNFC is designed respectively. Based on Lyapunov theory, the controller design is transformed into a multi-objective convex optimization problem based on linear matrix inequalities (LMIs). Simulation results based on the on-orbit servicing spacecraft show good performance under external disturbances, model parameter uncertainty and controller perturbation, which validates the effectiveness and feasibility of the proposed control method.

This paper investigates the robust non-fragile state feedback attitude control problem for uncertain spacecraft, and the problem explored here is subject to H∞ performance constraint, quadratic sta...

Because the artificial bee colony(ABC) algorithm has problems of poor development ability, slow convergence rate and easy to fall into local optimum, an improved ABC algorithm is proposed. The algorithm combines the global optimal guidance strategy, we propose a new location update formula to improve the efficiency of the iterative optimization process, and introduce the mining coefficient in the formula to improve the accuracy of global optimization. In addition, the beta distribution is introduced during the scouting bee phase, which improves the disturbance ability of the algorithm and prevents it from falling into local extremum effectively. Finally, the simulation results of four benchmark functions of Sphere, Rastrigin, Rosenbrock and Griewank show that compared with the traditional algorithm and other improved artificial bee colony algorithm, the proposed algorithm has faster convergence speed and better optimization ability.

A novel model of space electromagnetic docking is established to solve the problem of traditional plume contamination and short service life in this paper. A new modelling technique regarding space electromagnetic docking for CubeSat is proposed. The mathematical model with four core-containing energized solenoids is derived and simplified to the magnetic dipole far-field model of single-turn coil. Simplifying the magnetic dipole far-field model will yield the model of space electromagnetic docking, and the numerical simulation results also verify the reliability and feasibility of simplification process.