This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output LPV models Identification, set membership methods, optimization methods and subspace methods for state-space LPV models identification and orthonormal basis functions methods. Since there is a strong connection between LPV systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.

The scientific research in many engineering fields has been shifting from traditional first-principle-based to data-driven or evidence-based theories. The latter methods may enable better system design, based on more accurate and verifiable information. In the era of big data, IoT and cyber-physical systems, this subject is of growing importance, as data-driven approaches are key enablers to solve problems that could not be addressed by standard approaches. This book presents a number of innovative data-driven methodologies, complemented by significant application examples, to show the potential offered by the most recent advances in the field. Applicable across a range of disciplines, the topics discussed here will be of interest to scientists, engineers and students in automatic control and learning systems, automotive and aerospace engineering, electrical engineering and signal processing.

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.