Showing papers by "Juan I. Yuz published in 2013"

Sampling and Sampled-Data Models: The Interface Between the Continuous World and Digital Algorithms

Abstract: Modern signal processing and control algorithms are invariably implemented digitally, yet most real-world systems evolve in continuous time. Hence, the interaction between sampling and the behavior of continuous-time systems is an important ingredient in all real-world signals and systems problems.

Orthonormal basis functions applied to optimal control with pole location constraints

Abstract: This article presents a novel optimal control design technique based on a quadratic performance index, and using orthonormal basis functions to achieve prescribed closed loop poles. These functions are introduced through a Youla parametrization for the stable and unstable plant case, thus forcing a structural constraint into the controller.

Sampling Zeros of Discrete Models for Fractional Order Systems

Abstract: Most real systems evolve in continuous-time and are modeled using differential equations. However, (discrete-time) sampled-data models are necessary to describe the interaction with digital devices. For rational transfer functions, with integer-order derivatives, a well known consequence of the sampling process is the presence of sampling zeros. In this note we extend this result to systems described in terms of fractional-order derivatives. Specifically we define fractional-order Euler-Frobenius polynomials and we use them to characterize the asymptotic sampling zeros for fractional systems as the sampling period tends to zero.

Vector Measures of Accuracy for Sampled Data Models of Nonlinear Systems

Abstract: In this technical note, we introduce several novel vector measures of accuracy for sampled-data nonlinear models. The new definitions of truncation error assign a unique error bound to each component of the state vector. We argue that this new definition of truncation error is well suited to control and system identification problems where certain combinations of states, e.g., the system output, are of particular interest. We apply the new measures of accuracy to a recently developed model described in and establish several associated properties which were previously unrecognized.

Continuous-time system identification of a ship on a river

Abstract: Model-based control strategies require accurate modeling of a system. Physical modeling leads to differential equations where the parameters can then be estimated from experimental data. In this paper, we present the continuous-time identification of the ship dynamics based on real data collected in open loop. In particular, the models for the drift and yaw dynamics are estimated for one ship. The obtained models show good results when tested with validation data and could be used, for example, for autopilot control strategies.

Model validation methods for errors-in-variables estimation

Abstract: When identifying a dynamic system the model has to be validated as well. For an errors-in-variables situation where both input and output measurements are noise corrupted, this is a nontrivial task, seldom treated in the literature. Some different approaches for model validation are introduced and evaluated by theoretical analysis as well as application to simulated data.