Showing papers by "Juan I. Yuz published in 2016"

Real-time identification of linear continuous-time systems with slowly time-varying parameters

Abstract: The on-line recursive estimation of linear time-varying systems usually involves discrete-time models. In the case of continuous-time models, recursive off-line estimation has been considered in some detail but on-line approaches based on continuous-time models have received less attention, partly due to the increased complexity associated with the need to handle the time-derivatives of the input and output variables. In this study, we present a real-time instrumental variable technique where linear filters are used to handle the time derivatives and the parameter variations are represented by a stochastic model.

Sampled Data Models for Nonlinear Stochastic Systems: Truncation Errors and Sampling Zero Dynamics

Abstract: In this paper, we consider nonlinear stochastic systems and intersect ideas from nonlinear control theory and numerical analysis. In particular, we use the idea of relative degree. This concept guarantees smoothness properties of the output and this, in turn, allows one to establish properties that are unique to the control-theoretic perspective. The contributions of the current paper are threefold. Firstly, we define different error measures that extend the ideas of local and global approximation errors for nonlinear stochastic systems. Secondly, we demonstrate that the concept of relative degree plays a key role in obtaining higher order of accuracy for integration procedures compared to Euler-Maruyama integration. We show that a particular state-space model, named STTS model, has an improved order of accuracy when compared to an Euler-Maruyama approximation, at no significant extra computational cost. Thirdly, we show that a further approximation to the STTS model, named MSTTS model, while retaining the order of local errors, has explicit sampling zero dynamics, associated with the noise processes, that have no continuous-time counterpart. The extra zero dynamics are shown to be a function of the Euler-Frobenius polynomials. To the best of the authors' knowledge, this is the first reference to sampling zero dynamics for stochastic nonlinear systems.

Revisiting the EKF concept for low speed sensorless control of cage induction motors

Abstract: The use of the EKF for induction machine sensorless control is revisited with focus on the improvement of the method for low speed operation. An appropriate discretization method is discussed, the converter non-linearities are compensated for and the observer includes stator resistance estimation. Sensorless speed closed loop experimental results show stable operation down to standstill in motoring and stable transition through zero frequency in any mode of operation. Limitations are observed for permanent operation near or at zero frequency in regenerative operation, consistent with well known theoretical limitations.

Application of point processes estimation to a Metro system

Abstract: Metro systems are one of the most common transportation method in major cities. As a consequence, modeling and simulation of metro systems under different conditions are important to improve their performance. In this paper, we focus on the modeling of the arrival of passengers on a simple Metro line. In particular, we propose to estimate the parameters of two different point process models for the passenger arrival: a non-homogeneous Poisson process and a Hawkes-Phan process. We present preliminary numerical results based both on real data and data generated using a simulator developed by the authors jointly with MetroValparaiso, Chile.