Spacecraft Dynamics and Control: The Embedded Model Control Approach
Mar 2018 · Butterworth-Heinemann
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790
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About this ebook
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. - The book begins with an extensive introduction to attitude geometry and algebra and ends with the core themes: state-space dynamics and Embedded Model Control - Fundamentals of orbit, attitude and environment dynamics are treated giving emphasis to state-space formulation, disturbance dynamics, state feedback and prediction, closed-loop stability - Sensors and actuators are treated giving emphasis to their dynamics and modelling of measurement errors. Numerical tables are included and their data employed for numerical simulations - Orbit and attitude control problems of the European GOCE mission are the inspiration of numerical exercises and simulations - The suite of the attitude control modes of a GOCE-like mission is designed and simulated around the so-called mission state predictor - Solved and unsolved exercises are included within the text - and not separated at the end of chapters - for better understanding, training and application - Simulated results and their graphical plots are developed through MATLAB/Simulink code
About the author
Enrico Canuto taught Automatic Control for more than 40 years at Politecnico di Torino, Italy. He developed and applied the embedded model control methodology for the design and implementation of digital control systems. Over the course of his career, he has contributed to data reduction of the European astrometric mission Hipparcos, concluding with the publication of the Hipparcos star Catalogue of 120,000 stars; to the European GOCE mission and other forthcoming missions; to instruments for space qualification like the Nanobalance thrust-stand. In the last ten years, he has also collaborated with the Center for Gravity Experiments, Huazhong University of Science and Technology, Wuhan, and the Tianqin Centre, Sun-Yat-Sen University, Zhuhai, China, in the field of scientific space missions aimed at detecting gravitational waves.Carlo Novara received his MSc degree in Physics from the University of Turin in 1996 and his PhD degree in Computer and System Engineering from Politecnico di Torino in 2002. He held a visiting researcher position at University of California at Berkeley in 2001 and 2004 and is currently a Full Professor at Politecnico di Torino. He is a really prolific author of peer-reviewed scientific publications in international journals and conference proceedings. He has been involved in several national and international projects and in several research contracts in collaboration with Italian and European companies. He is the co-author of several patents in the automotive field. He is a member of the IEEE Technical Committee on System Identification and Adaptive Control, of the IFAC Technical Committee on Modelling, Identification and Signal Processing, of the IFAC Technical Committee on Modelling and Control of Environmental Systems, and a founding member of the IEEE-CSS Technical Committee on Medical and Healthcare Systems. His research interests include nonlinear and linear parameter-varying system identification, filtering/estimation, time series prediction, nonlinear control, predictive control, data-driven methods, set membership methods, sparse methods, nonlinear optimization, and aerospace, automotive, biomedical, and energy applications.Donato Carlucci is an Associate Professor at Politecnico di Torino. He is the author or co‐author of scientific publications on applied nonlinear systems control. He has been involved in national and international projects in collaboration with Italian and European industries. He has worked for more than 40 years in the fields of nonlinear system control including aerospace systems. His current research interests include satellite attitude control. He is teaching courses on Automation and Production Systems at Politecnico di TorinoCarlos Perez-Montenegro received his MSc degree in Electronic Engineering from Pontificia Universidad Javeriana in 2007, followed in 2014 by a PhD in Computer and Control Engineering from the Department of Control and Computer Engineering of Politecnico di Torino. His research interests span the fields of guidance, navigation, and control (GNC), with a focus on applications to planetary landing systems and unmanned aerial systems. Currently, he is designing and implementing control and estimation systems for service instruments of the automotive industry.
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