The field of automatic control has seen tremendous advances in the past years, driven by the increasing availability of capable computers for small and em- bedded systems with tight constraints on cost, energy and size. Sophisticated nonlinear filters such as the Extended Kalman Filter can be run even on average embedded hardware, and complex optimizing control schemes such as Model Predictive Control can be precomputed and thus executed on tiny systems in real time. Despite the availability of promising new theories and algorithms, the practitioner often finds it hard to apply these methods to the problems at hand. One of the problems lies in the fact that the theoretical frameworks are relatively involved, rendering the adaptation of many theoretical algorithms to practical problems difficult. Many estimation and control algorithms employ a dynamical model of the system under consideration. Augmented models are a promising tools for extending such algorithms to fit practical problems. Augmenting a model thereby describes the act of appending additional dynamics to an already existing model of the system. These augmented dynamics may for instance represent disturbances acting on the system, or they may be used to model varying parameters of the dynamic equations of the system. The advantage of augmented models is that they do not conceptually change the underlying algorithm, thus retaining most or all of its properties. This thesis strives to give an overview of the uses of augmented models in estimation and control, and to provide several methods which facilitate the adaptation of existing algorithms to practical problems. The first part discusses two practical case studies where augmented models are used in an estimation context. The second part deals with offset-free reference tracking for Model Predictive Control.
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Book Description CreateSpace Independent Publishing Platform, 2011. Paperback. Book Condition: Used: Good. Bookseller Inventory # SONG1453842616