Iterative learning control is a feedforward control technique applied to systems or processes that operate in a repetitive fashion over a fixed interval of time to improve tracking/regulation performance in response to reference inputs/disturbance inputs that are repeatable in each cycle. In this paper, learning control is applied to coil-to-coil gauge and tension control during the thread-up p...

This thesis proposal discusses control of dynamic systems and its relation to time. Although much research has been done concerning control of dynamic systems and decision making, little research exists about the relationship between time and control. Control is defined as the ability to keep a target system/ process in a desired state. In this study, properties of time such as fast, slow, over...

The present report summarizes the work conducted during the internship on Feedforward Control of the Magnetic Levitation Setup. Different feedforward strategies, specifically tailored for this setup, are developed and reviewed. These feedforward methods explicitly take the intrinsic position-dependent behavior of the magnetic levitation setup into account. Additionally, closed-loop stability of...

The system operates in two modes : bootstrap and feed forward mode. In bootstrap mode a portion of the image is segmented, the texture boundary is located, and all of the parameters necessary for configuring the system are found. These include the resolution of the segmentation, identification of the most effective features for distinguishing between the two textures, and an estimate of the tex...

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods require a slow and memoryconsuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convo...

Even for models of very simple networks, like those in Chapters 2 and 3, describing the system requires many parameters. These parameters are often unknown or uncertain. Consequently, predicting the response of a gene circuit may require infering gene circuit function from data on circuit structure alone. By using the feed-forward loop as a model system, this chapter introduces a technique for ...

A new approach to robust estimation of signals, prediction of time{series and robust feedforward control is considered. Signal and system parameter deviations are represented as random variables, with known covariances. A robust design is obtained by minimizing the squared estimation error, averaged both with respect to model errors and the noise. A polynomial equations approach, based on avera...

In this chapter we study two naturally occurring feed-forward loops that are involved in galactose metabolism and transport. Despite having network structures that are capable of a producing dynamic, temporally diverse responses we find, by measuring dynamic noise correlations, that in their natural context these feed-forward loops are inactive. By perturbing genetic conditions the activity can...