Adaptive Control: Actual Status and Trends

Important progress in research and application of Adaptive Control Systems has been achieved in the last ten years. The techniques which are currently used in applications will be reviewed. Theoretical aspects currently under investigation and which are related to the application of adaptive control techniques in various fields will be briefly discussed. Applications in various areas will be briefly reviewed. The use of adaptive techniques for vibrations monitoring and active vibration control will be emphasized

Imaging of buried objects from experimental backscattering time dependent measurements using a globally convergent inverse algorithm

We consider the problem of imaging of objects buried under the ground using backscattering experimental time dependent measurements generated by a single point source or one incident plane wave. In particular, we estimate dielectric constants of those objects using the globally convergent inverse algorithm of Beilina and Klibanov. Our algorithm is tested on experimental data collected using a microwave scattering facility at the University of North Carolina at Charlotte. There are two main challenges working with this type of experimental data: (i) there is a huge misfit between these data and computationally simulated data, and (ii) the signals scattered from the targets may overlap with and be dominated by the reflection from the ground's surface. To overcome these two challenges, we propose new data preprocessing steps to make the experimental data to be approximately the same as the simulated ones, as well as to remove the reflection from the ground's surface. Results of total 25 data sets of both non blind and blind targets indicate a good accuracy.Comment: 34 page

Generalized Stochastic Gradient Learning

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both di1er from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity