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This paper investigates a tracking control issue for a class of drag-free spacecraft with state constraints caused by the optical assembly. Firstly, a nonlinear kinematics and dynamics relative equation of coupling system of position and attitude is devised by a 6-degree of freedom (DOF) model of rigid body. Secondly, an optimal control method with a terminal functional is designed to meet state constraints. To solve the Hamilton-Jacobi-Bellman (HJB) equation generated by the optimal control of the nonlinear model, a policy iteration ideal is presented and the final numerical solution of the iteration is obtained by a critic neural network (NN). Thirdly, to enhance the robustness of the closed-loop system, an optimal sliding mode control is proposed, which can ensure the optimal and robustness performance simultaneously. At last, the simulation results demonstrate the performance of the proposed methods and the contrast of precision and robustness of two methods are showcased. [ABSTRACT FROM AUTHOR]

• Space debris detection; • Payload camera breadboard for space surveillance; • Star tracking and space debris detection algorithms; • Hybrid time-index image algorithm. Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth's orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms' tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net , to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost. [ABSTRACT FROM AUTHOR]

This paper addresses the multi-objective integrated attitude control problem of a large solar power satellite (SPS) in the presence of time-varying parameters, disturbances, and actuator saturation. The attitude coupling dynamic model, which could accurately describe the rotational motion of different SPS components, is firstly obtained. The gravity gradient, solar radiation pressure, microwave radiation reflection, and friction torques are investigated to evaluate their influence on attitude control accuracy. The coupling dynamic model is linearized and discretized, which provides a subsystem model for distributed controller design. A distributed incremental predictive controller is designed for each rotation channel to form a distributed system. The augmented system state is defined to solve the complex constraint problem, and the predicted value with feedback correction is introduced to deal with the disturbances. Besides, the stability of the system is analyzed by considering external disturbances and actuator failure. Comparative analysis with centralized methods under the same prediction horizon is provided, and the results demonstrate a tenfold solution performance enhancement while maintaining accuracy. Furthermore, the controller is integrated with feedback correction, which could improve control accuracy by at least one order of magnitude. The proposed controller reduces the adjusting time for attitude stabilization by 56.8 % in the presence of actuator failure. The simulation results demonstrate that the proposed controller has good robustness and fault tolerance, which provides a feasible solution for SPS multiple attitude control missions. [ABSTRACT FROM AUTHOR]

A modified unscented Kalman filter is presented to estimate the quaternion parameters as well as the angular velocities of a rigid gyro-less satellite under faulty sensor conditions. The task is carried out using the Sun sensor and magnetometers as attitude sensors with bounded noise and unknown fault(s). Following the presentation of the satellite attitude dynamics and filtering formulations, a new fault detection and isolation algorithm is proposed. The latter is based on a modified unscented Kalman filter structure for improved fault detection, sensor isolation, and attitude control (AC). A Backtracking Search Algorithm (BSA) is also used to design and optimize the PID controller gains, as well as to optimize sensor installation orientation for residual signal decoupling. In this regard, the paper presents and proves a theory that describes how the proposed optimization process for sensor installation can analytically decouple the faulty orientation signal from the healthy ones. A comparison of attitude and angular velocity estimation approaches using the standard Kalman, extended Kalman, and a proposed modified unscented Kalman filter is also provided. The results show that the proposed filter performs significantly better in closed-loop AC systems and copes adequately with sensor faults. • Gyro-less satellite attitude estimation and control with faulty sensors is studied. • A modified unscented Kalman filter is proposed and compared with KF and EKF. • A novel fault detection and isolation (FDI) algorithm is developed. • FDI is optimized with respect to sensor installation (SI) for improved accuracy. • A theorem is proven that demonstrates the decupling of the residual signals. [ABSTRACT FROM AUTHOR]

There is a growing concern about the pollution of agricultural systems caused by plastic residue and debris. This pollution poses a threat to the quality of food products. To gain a deeper understanding of how to maintain cleaner farmlands and reduce plastic residue and debris pollution, this cross-sectional study investigated the intention and behavior of plastic residue management (BPRM) in farmlands. Using random sampling method, the study surveyed 338 farmers in northwestern Iran. The resulting evidence found that 37.6% of farmers perform the BPRM with a mean score above average. The path analysis cleared that among the conceptualized ecological, economic, and social connotations, only the ecological attitude had a significant and positive impact on the BPRM (β E c o A t t i → B H V = 0.28, p < 0.001). It was found that both fact feedback (β F F B s → Int = 0.76, p < 0.001) and causal feedback (β C F B s → Int = 0.22, p < 0.001) contribute to reducing plastic residue pollution and forecasting the intention to perform the BPRM. The intention, in turn, directly predicts BPRM (β Int → B H V = 0.30, p < 0.001). Using a 2 × 2 cross-tabulation of causal feedback and the scenario of landfilling, derived from perceived action-based life cycle analysis of plastic wastes, the association between the two variables was found to be statistically significant (x 2 (df) = 3.24(1), p < 0.05). The resulting evidence would provide essential information for farm management strategists and governmental authorities to reduce plastic residue pollution in farmlands. This can be achieved by implementing policy initiatives that aim to change farmers' intentions and behaviors. • Paper indicates how to keep farmlands cleaner from plastic residue pollution. • Factual feedback forecasts intention to perform plastic residue management behavior. • Cause feedback predicts intention to perform plastic residue management behavior. • Cause feedback links with scenario of landfilling from action-based life cycle. • Factual and causal feedback should be given to farmers. [ABSTRACT FROM AUTHOR]

• Entropy functions based on Perceived Uncertainty are introduced. • The extant entropy functions are special cases of the proposed functions. • Analysis of the special cases is performed. • Applicability in the real world decision-making is demonstrated. In this paper, new entropy functions are formulated based on an agent's perceived uncertainty that inevitably affects the agent's choice. The role of the decision-maker's (DM's) attitude is emphasized as one of the key determinants of such an entropy function. More specifically, new attitude-based variants of Shannon's, Pal & Pal, and Aggarwal's probabilistic entropies are introduced. The extant fuzzy entropies are also extended to consider the agent's specific attitude. The proposed entropy functions provide a wide range of entropy values with the conventional entropy functions as their special cases. The special cases of the proposed entropies are examined. The wide applicability of the proposed entropy functions in multi criteria decision making is highlighted. A case-study is included to showcase the usefulness of the proposed entropy functions in the real world. [ABSTRACT FROM AUTHOR]