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" Spacecraft dynamics and control : "
Enrico Canuto, Carlo Novara, Luca Massotti, Donato Carlucci, Carlos Perez Montenegro.
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BL
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| Record Number
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872256
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| Main Entry
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Canuto, Enrico
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| Title & Author
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Spacecraft dynamics and control : : the embedded model control approach /\ Enrico Canuto, Carlo Novara, Luca Massotti, Donato Carlucci, Carlos Perez Montenegro.
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| Publication Statement
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Cambridge, MA :: Butterworth-Heinemann,, [2018]
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| Series Statement
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Aerospace engineering
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| Page. NO
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1 online resource
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| ISBN
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0081017952
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: 9780081017951
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0081007000
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9780081007006
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Includes bibliographical references and index.
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| Contents
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1. Introduction; 2. Attitude Representation; 3. Orbital Dynamics; 4. The Environment: Perturbing Forces and Torques; 5. Perturbed Orbital Dynamics; 6. Attitude Kinematics: Modeling and Feedback; 7. Attitude Dynamics: Modeling and Control; 8. Orbit and Attitude Sensors; 9. Orbit and Attitude Actuators; 10. Attitude Determination; 11. Orbital Control and Prediction Problems; 12. Attitude Control: A Case Study; 13. Introduction to Dynamic Systems; 14. Introduction to Embedded Model Control; Index.
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| Abstract
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Spacecraft Dynamics and Control: The Embedded Model Control Approach provides a uniform and systematic way of approaching space engineering control problems from the standpoint of model-based control, using state-space equations as the key paradigm for simulation, design and implementation. The book introduces the Embedded Model Control methodology for the design and implementation of attitude and orbit control systems. The logic architecture is organized around the embedded model of the spacecraft and its surrounding environment. The model is compelled to include disturbance dynamics as a repository of the uncertainty that the control law must reject to meet attitude and orbit requirements within the uncertainty class. The source of the real-time uncertainty estimation/prediction is the model error signal, as it encodes the residual discrepancies between spacecraft measurements and model output. The embedded model and the uncertainty estimation feedback (noise estimator in the book) constitute the state predictor feeding the control law. Asymptotic pole placement (exploiting the asymptotes of closed-loop transfer functions) is the way to design and tune feedback loops around the embedded model (state predictor, control law, reference generator). The design versus the uncertainty class is driven by analytic stability and performance inequalities. The method is applied to several attitude and orbit control problems.
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| Subject
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Space vehicles-- Control systems.
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Space vehicles-- Dynamics.
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Space vehicles-- Control systems.
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Space vehicles-- Dynamics.
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.411
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| LC Classification
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TL1050.C36 2018eb
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| Added Entry
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Carlucci, Donato
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Massotti, Luca
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Montenegro, Carlos Perez
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Novara, Carlo
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