Observation of force-detected nuclear magnetic resonance in a homogeneous field

We report the experimental realization of BOOMERANG (better observation of magnetization, enhanced resolution, and no gradient), a sensitive and general method of magnetic resonance. The prototype millimeter-scale NMR spectrometer shows signal and noise levels in agreement with the design principles. We present H-1 and F-19 NMR in both solid and liquid samples, including time-domain Fourier transform NMR spectroscopy, multiple-pulse echoes, and heteronuclear J spectroscopy. By measuring a H-1-F-19 J coupling, this last experiment accomplishes chemically specific spectroscopy with force-detected NMR. In BOOMERANG, an assembly of permanent magnets provides a homogeneous field throughout the sample, while a harmonically suspended part of the assembly, a detector, is mechanically driven by spin-dependent forces. By placing the sample in a homogeneous field, signal dephasing by diffusion in a field gradient is made negligible, enabling application to liquids, in contrast to other force-detection methods. The design appears readily scalable to µm-scale samples where it should have sensitivity advantages over inductive detection with microcoils and where it holds great promise for application of magnetic resonance in biology, chemistry, physics, and surface science. We briefly discuss extensions of the BOOMERANG method to the µm and nm scales

How to get a conservative well-posed linear system out of thin air. Part II. Controllability and stability

Published versio

On the dynamic behavior and stability of controlled connected Rayleigh beams under pointwise output feedback

We study the dynamic behavior and stability of two connected Rayleigh beams that are subject to, in addition to two sensors and two actuators applied at the joint point, one of the actuators also specially distributed along the beams. We show that with the distributed control employed, there is a set of generalized eigenfunctions of the closed-loop system, which forms a Riesz basis with parenthesis for the state space. Then both the spectrum-determined growth condition and exponential stability are concluded for the system. Moreover, we show that the exponential stability is independent of the location of the joint. The range of the feedback gains that guarantee the system to be exponentially stable is identified

Mathematical Modeling, Motion Planning and Control of Elastic Structures with Piezoelectric Elements

The objective of this work is the development of a motion planning and tracking control approach for elastic structures. Motivated by the morphing wing concept of the field of aerospace engineering a so-called “smart wingsail” defines the center of the presented research. The motion planning and tracking control approach has to achieve different rest-to-rest motions of the wingsail’s transversal displacement. The design of the mechanical structure as well as the control concept of the wingsail relies on the results of proof of concept studies. For this purpose, different systems of interconnected bending beams are considered which emulates parts of the wingsail. The development of the model based control approaches requires an accurate system description. The modeling itself is done by an analytic energy based approach for the beams’ systems, where for the wingsail the finite elements method is used due to the risen complexity of the curved structure. To achieve a precise description of the governing dynamics different parameter identification concepts are discussed and applied. This leads to a precise but rather complex system description which covers the measured behavior of the experimental setups. Considering the objective of a real time capable control approach the complexity has to be reduced without a significant loss of accuracy. For this purpose different model order reduction techniques are discussed and applied. The resulting systems models are the bases of the control designs. Two different control concepts are presented and evaluated. At first the two-degrees-of-freedom control approach is introduced which combines a flatness-based feedforward control approach with a feedback controller. On the other hand, the so-called model predictive control approach is presented which is based on the solution of an optimization problem. Both concepts are evaluated by numeric analyses and by experiments