PrefaceThe motivation for the notes presented in this volume of BCAM SpringerBriefs comes from a multidisciplinary graduate course offered to students in Mathematics, Physics or Control Engineering (at the University of Burgundy, France and at the Institute of Mathematics for Industry Fukuoka, Japan). The content is based on two real applications, which are the subject of current academic research programs and are motivated by industrial uses. The objective of these notes is to introduce the reader to techniques of geometric optimal control as well as to provide an exposure to the applicability of numerical schemes implemented in HamPath [32], Bocop [19] and GloptiPoly [47] software.To highlight the main ideas and concepts, the presentation is restricted to the fundamental techniques and results. Moreover the selected applications drive the exposition of the different methodologies. They have received significant attention recently and are promising, paving the way for further research by our potential readers. The applications have been chosen based on the existence of accurate mathematical models to describe them, models that are suitable for a geometric analysis, and the possibility of implementing results from the analysis in a practical manner.The notes are self-contained, moreover, the simpler geometric computations can be reproduced by the reader using our presentation of the maximum principle. The weak maximum principle covers the case of an open control domain which is the standard situation encountered in the classical calculus of variations, and is suitable for analysis of the first application, motility at low Reynolds number, although a good understanding of the so-called transversality conditions is necessary. For the second application, control of the spin dynamics by magnetic fields in nuclear magnetic resonance, the use of the general maximum principle is required since the control domain is bounded. At a more advanced level, the reader has to be familiar with the numerical techniques implemented in the software used for our calculations. In addition, symbolic methods have to be used to handle the more complex computations.The first application is the swimming problem at low Reynolds number describing the swimming techniques of microorganisms. It can be easily observed in nature, but also mechanically reproduced using robotic devices, and it is linked to v vi Preface medical applications. This example serves as an introduction to geometric optimal control applied to sub-Riemannian geometry, a non-trivial extension of Riemannian geometry and a 1980's tribute of control theory to geometry under the influence of R. Brockett [31]. We consider the Purcell swimmer [78], a three-link model where the shape variables are the two links at the extremities and the displacement is modeled by both the position and the orientation of the central link representing the body of the swimmer. To make a more complete analysis in the framework of geometric control, we use a simplified model from D. Takagi called the ...