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Fast and accurate tracking of periodic reference trajectories is highly desirable in many nanopositioning applications, including scanning probe microscopy. Performance in common positioning stage designs is limited by the presence of lightly damped resonances, and actuator nonlinearities such as hysteresis and creep. To improve the tracking performance in such systems, several damping and tracking control schemes have been presented in the literature. In this paper, five different control schemes are presented and applied to a nanopositioning system for experimental comparison. They include schemes applying damping control in the form of positive position feedback, integral resonant control, integral force feedback, and passive shunt-damping. Also, a control scheme requiring only a combination of a low-pass filter and an integrator is presented. The control schemes are fixed-structure, low-order control laws, for which few results exist in the literature with regards to optimal tuning. A practical tuning procedure for obtaining good tracking performance for all of the presented control schemes is therefore presented. The schemes provide similar performance, and the main differences are due to the specific implementation of each scheme.

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Identification of mechanical properties of cells has previously been shown to have a great potential and effectiveness on medical diagnosis. As a result, it has gathered increasing interest of researchers over the recent years. Atomic force microscopy has become one of the prime technologies for obtaining such properties. Traditionally, local variations in elasticity has been obtained by mapping contact force during sample indentation to static Hertzian contact models. More recently, multiharmonic methods have allowed for both viscous and elastic measurements of soft samples. In this article, a new technique is presented based on dynamic modeling and identification of the sample. Essentially, the measured signals are mapped to the sample properties of the model in a least-square sense. This approach allows for easy extensibility beyond pure viscoelastic measurements. Furthermore, an iterative modeling approach can be used to best describe the measured data. The technique can be operated in either dynamic indentation viscoelastic mode, or scanning viscoelastic mode. First, a dynamic, viscoelastic model of the sample is presented. Then, the parameter identification method is described, showing exponential convergence of the parameters. A simulation study demonstrates the effectiveness of the approach in both modes of operation.

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The concept of autonomous mobile robots (AMR) has gained much popularity in recent years, particularly in commercial settings where the name industrial autonomous mobile robot (IAMR) is proposed. In addition to automatic guided vehicles and automated mining trucks, IAMR also includes autonomous merchant ships. AMR is an old concept which was first introduced in the 1980s. Although the concept of AMRs is old and broadly used, there is still no common definition of autonomy when mobile robots are concerned. This paper will review some of the most known definitions and develop a taxonomy for autonomy in mobile autonomous robots. This will be used to compare the different definitions of robotic autonomy. This paper will mainly look at industrial autonomous mobile robots, i.e. systems that are designed to operate with a clear commercial objective in mind and which are normally supported by a remote control centre. This means that the robot is not fully autonomous, but to varying degrees dependent on humans in some control and monitoring functions.