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The objective of this paper is to show how experimentally based modelling can be used for designing Linear Parametrically Varying (LPV) controllers. As a test system we use an industrial pick and place unit with one linear X-drive and two independent linear Ydrives. The dynamics of the Y-axes depend on the Xposition. An LPV model is derived by using measured Frequency Response Functions at different positions, fitting a parametric model on each measurement and combining these models by linking parameters via a fit as a function of operating point. Rewriting the LPV model into a LFT structure and applying model reduction in the space of the scheduling variable finalizes the modelling phase. With this model an LPV controller is calculated and shows robust performance for the whole operating range, in contrast to local H , controllers.

This paper presents a frequency domain identification of dynamic model parameters for frictional presliding behavior. The identification procedure for the dynamic model parameters, i.e., 1) the stiffness and 2) the damping of the presliding phenomenon, is reduced from performing several dedicated experiments to one experiment where the system is excited with random noise and the frequency response function (FRF) of the phenomenon is measured. Time domain validation experiments on a servomechanism show accurate estimates of the dynamic model parameters for the linearized presliding behavior.

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Abstract-The design of a CACC system and corresponding experiments are presented. The design targets string stable system behavior, which is assessed using a frequency-domain-based approach. Following this approach, it is shown that the available wireless information enables small inter-vehicle distances, while maintaining string stable behavior. The theoretical results are validated by experiments with two CACC-equipped vehicles. Measurement results showing string stable as well as string unstable behavior are discussed.

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Abstract-This paper deals with data-based optimal control. The control algorithm consists of two complementary subsystems, namely a data-based observer and an optimal feedback controller based on the system's Markov parameters. These parameters can be identified on-line using only input/output data. The effectiveness of the resulting controller is evaluated with a regulation and a tracking control experiment, performed on a direct-drive robot of spatial kinematics.