Dual-Quaternion-Based Fault-Tolerant Control for Spacecraft Tracking With Finite-Time Convergence

Results are presented for a study of dual-quaternion-based fault-tolerant control for spacecraft tracking. First, a six-degrees-of-freedom dynamic model under a dual-quaternion-based description is employed to describe the relative coupled motion of a target-pursuer spacecraft tracking system. Then, a novel fault-tolerant control method is proposed to enable the pursuer to track the attitude and the position of the target even though its actuators have multiple faults. Furthermore, based on a novel time-varying sliding manifold, finite-time stability of the closed-loop system is theoretically guaranteed, and the convergence time of the system can be given explicitly. Multiple-task capability of the proposed control law is further demonstrated in the presence of disturbances and parametric uncertainties. Finally, numerical simulations are presented to demonstrate the effectiveness and advantages of the proposed control method

ADP-based spacecraft attitude control under actuator misalignment and pointing constraints

This paper is devoted to real-time optimal attitude reorientation control of rigid spacecraft control. Particularly, two typical practical problems - actuator misalignment and forbidden pointing constraints are considered. Within the framework of adaptive dynamic programming (ADP), a novel constrained optimal attitude control scheme is proposed. In this design, a special reward function is developed to characterize the environment feedback and deal with the pointing constraints. Notably, a novel argument term is introduced to the reward function for overcoming the inevitable difficulty in actuator misalignment. By virtue of the Lyapunov stability theory, the ultimate boundedness of state error and the optimality of the proposed method can be guaranteed. Finally, the effectiveness and performance of the developed ADP-based controller are evaluated by not only numerical simulations but also experimental tests with a hardware-in-loop platform

Learning-based 6-DOF control for autonomous proximity operations under motion constraints

This paper proposes a reinforcement learning (RL)-based six-degree-of-freedom (6-DOF) control scheme for the final phase proximity operations of spacecraft. The main novelty of the proposed method are from two aspects: 1) the closed-loop performance can be improved in real-time through the RL technique, achieving an online approximate optimal control subject to the full 6-DOF nonlinear dynamics of spacecraft; 2) Nontrivial motion constraints of proximity operations are considered and strictly obeyed during the whole control process. As a stepping stone, the dual-quaternion formalism is employed to characterize the 6-DOF dynamics model and motion constraints. Then, an RL-based control scheme is developed under the dual-quaternion algebraic framework to approximate the optimal control solution subject to a cost function and a Hamilton-Jacobi-Bellman equation. In addition, a specially designed barrier function is embedded in the reward function to avoid motion constraint violations. The Lyapunov-based stability analysis guarantees the ultimate boundedness of state errors and the weight of NN estimation errors. Besides, we also show that a PD-like controller under dual-quaternion formulation can be employed as the initial control policy to trigger the online learning process. The boundedness of it is proved by a special Lyapunov strictification method. Simulation results of prototypical spacecraft missions with proximity operations are provided to illustrate the effectiveness of the proposed method

Optimized data-driven prescribed performance attitude control for actuator saturated spacecraft

This article addresses the crucial requirements in spacecraft attitude control: prescribed performance guarantees under actuator saturation and real-time cost optimization. As an application-oriented study, an approximate optimal prescribed performance attitude control scheme is proposed for this objective. To be specific, the prescribed performance constraint is converted into the system dynamics and merged into the adaptive dynamic programming design philosophy. Subsequently, the online learning law is designed based on a special saturated HJB error, in which a dynamical scale is introduced to adjust the learning gain by measured data. It enhances learning efficiency and applicability. Then, uniformly ultimately bounded stability of the whole system is achieved with guaranteed convergence of optimization by the Lyapunov-based stability analysis. Finally, both numerical simulation and hardware-in-the-loop experiments demonstrate the superiority and effectiveness of the proposed method. These attributes and outcomes attained will promote the development of practical space missions

Learning-based attitude tracking control with high-performance parameter estimation

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests

Combination Analysis of a Radiomics-Based Predictive Model With Clinical Indicators for the Preoperative Assessment of Histological Grade in Endometrial Carcinoma

BackgroundHistological grade is one of the most important prognostic factors of endometrial carcinoma (EC) and when selecting preoperative treatment methods, conducting accurate preoperative grading is of great significance.PurposeTo develop a magnetic resonance imaging (MRI) radiomics-based nomogram for discriminating histological grades 1 and 2 (G1 and G2) from grade 3 (G3) EC.MethodsThis was a retrospective study included 358 patients with histologically graded EC, stratified as 250 patients in a training cohort and 108 patients in a test cohort. T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) were performed via 1.5-Tesla MRI. To establish ModelADC, the region of interest was manually outlined on the EC in an apparent diffusion coefficient (ADC) map. To establish the radiomic model (ModelR), EC was manually segmented by two independent radiologists and radiomic features were extracted. The Radscore was calculated based on the least absolute shrinkage and selection operator regression. We combined the Radscore with carbohydrate antigen 125 (CA125) and body mass index (BMI) to construct a mixed model (ModelM) and develop the predictive nomogram. Receiver operator characteristic (ROC) and calibration curves were assessed to verify the prediction ability and the degree of consistency, respectively.ResultsAll three models showed some amount of predictive ability. Using ADC alone to predict the histological risk of EC was limited in both the cohort [area under the curve (AUC), 0.715; 95% confidence interval (CI), 0.6509–0.7792] and test cohorts (AUC, 0.621; 95% CI, 0.515–0.726). In comparison with ModelADC, the discrimination ability of ModelR showed improvement (Delong test, P < 0.0001 for both the training and test cohorts). ModelM, established based on the combination of radiomic and clinical indicators, showed the best level of predictive ability in both the training (AUC, 0.925; 95% CI, 0.898–0.951) and test cohorts (AUC, 0.915; 95% CI, 0.863–0.968). Calibration curves suggested a good fit for probability (Hosmer–Lemeshow test, P = 0.673 and P = 0.804 for the training and test cohorts, respectively).ConclusionThe described radiomics-based nomogram can be used to predict EC histological classification preoperatively

Transforming Growth Factor β Receptor Type 1 Is Essential for Female Reproductive Tract Integrity and Function

The transforming growth factor β (TGFβ) superfamily proteins are principle regulators of numerous biological functions. Although recent studies have gained tremendous insights into this growth factor family in female reproduction, the functions of the receptors in vivo remain poorly defined. TGFβ type 1 receptor (TGFBR1), also known as activin receptor-like kinase 5, is the major type 1 receptor for TGFβ ligands. Tgfbr1 null mice die embryonically, precluding functional characterization of TGFBR1 postnatally. To study TGFBR1–mediated signaling in female reproduction, we generated a mouse model with conditional knockout (cKO) of Tgfbr1 in the female reproductive tract using anti-Müllerian hormone receptor type 2 promoter-driven Cre recombinase. We found that Tgfbr1 cKO females are sterile. However, unlike its role in growth differentiation factor 9 (GDF9) signaling in vitro, TGFBR1 seems to be dispensable for GDF9 signaling in vivo. Strikingly, we discovered that the Tgfbr1 cKO females develop oviductal diverticula, which impair embryo development and transit of embryos to the uterus. Molecular analysis further demonstrated the dysregulation of several cell differentiation and migration genes (e.g., Krt12, Ace2, and MyoR) that are potentially associated with female reproductive tract development. Moreover, defective smooth muscle development was also revealed in the uteri of the Tgfbr1 cKO mice. Thus, TGFBR1 is required for female reproductive tract integrity and function, and disruption of TGFBR1–mediated signaling leads to catastrophic structural and functional consequences in the oviduct and uterus

Partial Lyapunov Strictification: Dual Quaternion based Observer for 6-DOF Tracking Control

International audienceBased on the dual-quaternion description, a smooth six-degree-of-freedom observer is proposed to estimate the incorporating linear and angular velocity, called the dual angular velocity, for a rigid body. To establish the observer, some important properties of dual vectors and dual quaternions are established, additionally, the kinematics of dual transformation matrices is deduced, and the transition relationship between dual quaternions and dual transformation matrices is subsequently analyzed. An important feature of the observer is that all estimated states are ensured to be C ∞ continuous, and estimation errors are shown to exhibit asymptotic convergence. Furthermore, to achieve tracking control objectives, the proposed observer is combined with an independently designed proportional-derivative-like feedback control law (using full-state feedback), and a special Lyapunov "strictification" process is employed to ensure a separation property between the observer and the controller, which further guarantees almost global asymptotic stability of the closed-loop dynamics. Numerical simulation results for a prototypical spacecraft pose tracking mission application are presented to illustrate the effectiveness and robustness of the proposed method