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Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.

Description This book is about the theory and methods of controlling the temperature of a satellite. It is the first to present satellite thermal control as an organized engineering discipline systematically derived from the principles of heat transfer. The treatment is thorough but consciously readable and requires only basic prerequisite knowledge of physics and mathematics. Although the main thrust is to give spacecraft managers and systems engineers a background for directing and advising during the evolution of a satellite thermal design, there is enough to attract students and aerospace engineers of all specialties. For those already involved in satellite thermal control, the book provides a valuable source of data and a definitive reference to the problems and methods of solution encountered in their trade. All the illustrations and numerical examples refer directly to actual situations. The author uses his wide experience to explain many of the difficult points that have made thermal control an often confusing subject. Significant selections are cited from the innumerable publications that have appeared since the launch of Sputnik in 1957. In addition, the author draws on vital material that, for one reason or another, has never been reported in general publications. The result is a comprehensive textbook on one of the most essential aspects in the design of satellites.

In this book, analytic methods of orbit prediction and control are investigated for important low-thrust and impulsive orbit change and station-keeping applications, and the results compared with actual exact numerically-integrated counterparts to evaluate their accuracy and applicability. With one eye on future autonomous on-orbit navigation applications both in near-circular and general elliptic orbit, with minimal computational eff ort and high accuracy, the orbit prediction and control segments of such autonomous navigation systems are explored by also taking into account the shadowing eff ect where no thrust is allowed, such as for electric propulsion applications. This book shows that many of these orbit prediction and station-keeping problems can be solved entirely in analytic form with very high accuracy, and allow the practitioners to solve similar problems by tailoring them to their own applications, and to better understand and evaluate the pertinent variables and parameters that drive and infl uence the resulting description and evolution of the orbits.

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection.

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

The reaction flywheel is a crucial operational component within a satellite’s attitude control system. Enhancing the performance of the reaction flywheel speed control system holds significant importance for satellite attitude control. In this paper, an active disturbance rejection control (ADRC) approach is introduced to mitigate the impact of uncertain disturbances on reaction flywheel speed control precision. The reaction flywheel speed control system is designed as an ADRC controller due to the current challenge of measuring unknown disturbances accurately in the reaction flywheel system. To derive the rotor’s speed observation value and the estimated total disturbances value, the sampled data of the reaction flywheel rotor position and torque control signal are fed into the extended state observer. The estimated total disturbances value is compensated on feedforward control, which could mitigate significantly the effects of various nonlinear disturbances. The paper initially establishes the rationale behind the reaction flywheel ADRC controller through theoretical analysis, followed by analysis of the differences of performance of reaction flywheel control by the ADRC controller and the PID controller in MATLAB/SIMULINK. Simulation results demonstrate the evident advantages of the ADRC controller over the PID controller in terms of speed command tracking capability and disturbances suppression ability. Subsequently, the ADRC controller program and the PID controller program are implemented on the reaction flywheel control circuit, and experiments are conducted to contrast speed command tracking and disturbance suppression. Importantly, the experimental outcomes align with the simulation results.

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

This paper presents the development of low-cost methodologies to determine the attitude of a small, CubeSat-class satellite and a microrover relative to the sun's direction. The use of commercial hardware and simple embedded designs has become an effective path for university programs to put experimental payloads in space for minimal cost, and the development of sensors for attitude and heading determination is often a critical part. The development of two compact and efficient but simple coarse sun sensor methodologies is presented in this research. A direct measurement of the solar angle uses a photodiode array sensor and slit mask. Another estimation of the solar angle uses current measurements from orthogonal arrays of solar cells. The two methodologies are tested and compared on ground hardware. Testing results show that coarse sun sensing is efficient even with minimal processing and complexity of design for satellite attitude determination systems and rover navigation systems.

In this paper, an improved robust model predictive controller (RMPC) is proposed based on model reference adaptive system (MRAS). In this algorithm, using the MRAS a combinational RMPC controller for three degree freedom satellite is designed such that the effect of moment of inertia uncertainty and external disturbance is compensated on the stability and performance of closed loop system. Control law is a state feedback which its gain is obtained by solving a convex optimization problem subject to several linear matrix inequalities (LMIs). To avoid the actuators saturation an input constraint is incorporated as LMI in the mentioned optimization problem. In addition to, using the MRAS system the effect of input disturbance is rejected on the system.The advantages of this algorithm are needless to exact information from system's model, robustness against model uncertainties and external disturbance. Results from the simulation of the system with the proposed algorithm are presented and compared to generalized incremental model predictive control (GIPC). The results show that the suggestive controller is more robust than the GIPC method.

Support Vector Machine Regression is a nonlinear modeling method with a simple structure and shows excellent performance compared with other nonlinear-linear regression methods. Unfortunately, most users always select the SVMR parameters by rule of thumb, so they frequently fail to get the optimal model. This paper propose to use the immune genetic algorithm to adjust the SVMR parameters and use the RMSE of the cross validation as the fitness of IGA. At last, this method was applied to modeling satellite attitude control system to detect the faults of the system. Simulation shows the high fitting precision to the system models which insures the correctness of the fault detection.