This chapter studies the problem of fault estimation using a fast adaptive fault diagnosis observer. Note that the advance of observer-based fault diagnosis is outlined and the idea of fault class estimation is introduced and studied. A new form of the estimator bloc considered for this purpose is an Unknown Input Observer (UIO). This observer is designed for an unknown input and fault free sys...

This file contains additional problems that instructors may find useful. The problems originate from various examination questions on adaptation and learning in the graduate level course EE210A: Adaptation and Learning taught by the author at UCLA Electrical Engineering. The problems are generally designed to test deeper understanding of core concepts in adaptation and estimation theories. This...

This paper presents a speed estimation scheme based on second-order sliding-mode Super Twisting Algorithm (STA) and Model Reference Adaptive System (MRAS) estimation theory for Sensorless control of multiphase induction machine. A stator current observer is designed based on the STA, which is utilized to take the place of the reference voltage model of the standard MRAS algorithm. The observer ...

The global estimation problem of the drift function is considered for a large class of ergodic diffusion processes. The unknown drift S(·) is supposed to belong to a nonparametric class of smooth functions of order k ≥ 1, but the value of k is not known to the statistician. A fully data-driven procedure of estimating the drift function is proposed, using the estimated risk minimization method. ...

We describe a compressive imager that adapts the measurement basis based on past measurements within a sequential Bayesian estimation framework. Simulation study shows a 7% improvement in reconstruction performance compared to a static measurement basis. © 2011 Optical Society of America OCIS codes: 110.1758,110.1085,100.3190.

The theory of adaptive sequence detection incorporating estimation of channel and related parameters is studied in the context of maximum-likelihood (ML) principles in a general framework based on the expectation and maximization (EM) algorithm. A generalized ML sequence detection and estimation (GMLSDE) criterion is derived based on the EM approach, and it is shown how the per-survivor process...

Abstract: Accurate channel estimation is necessary for coherent detection and adaptive control techniques, e.g., adaptive modulation and demodulation, of multicarrier signals. In this paper, we study one-dimensional channel estimation in frequencyor time-domain and 2-dimensional channel estimation in time-frequency domain. It is shown that the frequency (time)-domain interpolation channel estim...

Parameter estimation in nonlinear systems is an important issue in measurement, diagnosis and modeling. The goal is to find a differentiator free on-line adaptive estimation algorithm which can estimate the internal unknown parameters of dynamic systems using its inputs and outputs. This thesis provides new algorithms for adaptive estimation and control of nonlinearly parameterized (NLP) system...

In the following article we investigate a new algorithm for additive sound synthzesis that extends the standard approach by means of explictely modeling a sound as a superposition of non stationary partials with time varying amplitude and frequency. Due to the extended model an adaptive parameter estimation is required. By means of a mathematical investigation we show, that it is possible to tr...