Nonlinear and distributed sensory estimation

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers

Nonlinear and distributed sensory estimation

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers

Active Feedforward Disturbance Control System

PatentNoise effects in a signal for driving a plant are reduced by generating a reference signal from the error signal. A signal generator generates a reference signal for input to a finite impulse response (FIR) filter. The error signal is produced by differencing the transfer function output and a disturbance signal. The error signal is input to the signal generator and to a least mean square calculator. The reference signal is input to a copy of the transfer function that outputs a modified reference signal. The modified reference signal is input to least mean square calculator. An LMS signal that updates the filter coefficients to minimize the mean square error is calculated and the LMS signal and the reference signal are input to the FIR filter with the FIR filter being arranged to process the LMS signal and the reference signal to minimize the error signal

IMECE2004-60039 IMPLEMENTATION OF HIGH BANDWIDTH SENSOR ARRAYS USING FEEDBACK MECHANISMS

ABSTRACT Proposed in this paper is a new method to implement the high operating bandwidth sensor arrays. In certain control applications, it is necessary that a high bandwidth sensor be used to improve the efficiency of feedback. The design of a single sensor with the desired high bandwidth may not be easy and economically feasible. It is shown that the idea of sensor arrays can be utilized to obtain a cost effective and efficient solution to the problem posed. It is discussed that an effective data fusion scheme is necessary in order to implement the proposed sensor array that consists of low bandwidth pass-band sensors with possible overlapping operating regions. Moreover, we point out that obtaining accurate sensor models may not be always easy in practice and this may make the proposed sensor arrays inapplicable for certain applications. To address this issue, a new implementation scheme that utilizes feedback mechanisms to combine multi-sensor data is developed. The proposed framework is validated using simulation examples

DETC2003/VIB-48512 NONLINEAR AVERAGING OF MULTI-SENSOR DATA

ABSTRACT Considered in this paper is a framework for combining multiple sensor data to obtain a single inference. The task of fusing multi-sensor data is very challenging when no information about the sensor or estimation models is available. Kalman Filtering and other model-based techniques cannot be used to obtain a reliable inference. Linear Averaging of data is probably the simplest technique available, however, there is no guarantee that the fused measurement is, in fact, the best estimation. The problem will be worsened if one or more sensor measurements are faulty. In this paper, we analyze this problem and propose an effective multi-sensor fusion methodology. It is shown that a reliable solution can be obtained by nonlinearly averaging the multiple measurements. The proposed technique is well suited to identify outliers in the sensor measurements as well as to detect faulty sensor measurements. The developed algorithm is versatile in the sense that prior knowledge or information about sensors can be easily incorporated to improve the accuracy further. Illustrative examples and simulation data are presented to validate the proposed scheme

IMECE2003-41487 ROBUST SIGNAL RECOVERY FROM DISTORTED NONLINEAR SENSOR DATA

ABSTRACT In an attempt to facilitate the design and implementation of memory-less nonlinear sensors, the signal reconstruction schemes are analyzed and necessary modifications are proposed to improve the accuracy and minimize errors in sensor measurements. The problem of recovering chirp signal from the distorted nonlinear output is considered and an efficient reconstruction approach is developed. Model uncertainty is a serious issue with any model-based algorithms and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is proposed