Regressor based adaptive infinite impulse response filtering

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 35-36.Acar, EmrahM.S

Regressor-based adaptive infinite impulse response filtering

Superior performance of fast recursive least squares (RLS) algorithms over the descent-type least mean square (LMS) algorithms in the adaptation of FIR systems has not been realized in the adaptation of IIR systems. This is the result of having noisy observations of the original system output resulting in significantly biased estimates of the system parameters when this noisy signal is used in the adaptive system. Here, we propose an adaptive IIR system structure consisting of two parts: a two-channel FIR adaptive filter whose parameters are updated by the rotation-based multichannel least squares lattice (QR-MLSL) algorithm, and an adaptive régresser that provides more reliable estimates to the original system output based on previous values of the adaptive system output and noisy observation of the original system output. Two different regressors are investigated, and robust ways of adaptation of the régresser parameters are proposed. The performances of the proposed algorithms are compared with composite régresser (CR) and bias remedy least mean square equation error (BRLE) algorithms that are LMS-type successful adaptation algorithms, and it is found that in addition to the expected convergence speedup, the proposed algorithms provide better estimates to the system parameters at low SNR value. In addition, the extended Kaiman filtering approach is tailored to our application. Comparison of the proposed regressor-based algorithms with the extended Kaiman filter approach revealed that the proposed approaches provide improved estimates in systems with abrupt parameter changes. ©1993 IEEE

Adaptive IIR filtering using the homotopy continuation method

The objective of this study is to develop an algorithmic approach for solving problems associated with the convergence to the local minima in adaptive IIR filtering. The approach is based on a numerical method called the homotopy continuation method;The homotopy continuation method is a solution exhaustive method for calculating all solutions of a set of nonlinear equations. The globally optimum filter coefficients correspond to the solutions with minimum mean square error. In order to apply the technique to the adaptive IIR filtering problem, the homotopy continuation method is modified to handle a set of nonlinear polynomials with time-varying coefficients. Then, the adaptive IIR filtering problem is formulated in terms of a set of nonlinear polynomials using the mean square output error minimization approach. The adaptive homotopy continuation method (AHCM) for the case of time-varying coefficients is then applied to solve the IIR filtering problem. After demonstrating the feasibility of the approach, problems encountered in the basic AHCM algorithm are discussed and alternative structures of the filter are proposed. In the development of the proposed algorithm and its variations, the instability problem which is a second disadvantage of IIR filters is also considered;Simulation results for a system identification example validate the proposed algorithm by determining the filter coefficients at the global minimum position. For further validation, the AHCM algorithm is then applied to an adaptive noise cancellation application in ultrasonic nondestructive evaluation. Ultrasonic inspection signal reflections from defects and material grain boundaries are considered. The AHCM algorithm is applied to the noise cancellation mode to filter out the material noise. The experimental results show that the proposed algorithm shows considerable promise for real as well as for simulated data

ECG noise reduction technique using Antlion Optimizer (ALO) for heart rate monitoring devices

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist and automatic heart disorder diagnosis systems. In this paper, the use of Antlion Optimization (ALO) for optimizing and identifying the cutoff frequercy of ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed using the moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO with various parameters is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent AL0-based system successfully denoised the ECG signals more effectively than the conventional method. The percentage of the accuracy increased by 2%

Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

Learning-based nonlinear model predictive control with accurate uncertainty compensation

A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing