Abstract: The permanent magnet synchronous motor (PMSM) is
very useful in many applications. Vector control of PMSM is popular
kind of its control. In this paper, at first an optimal vector control for
PMSM is designed and then results are compared with conventional
vector control. Then, it is assumed that the measurements are noisy
and linear quadratic Gaussian (LQG) methodology is used to filter
the noises. The results of noisy optimal vector control and filtered
optimal vector control are compared to each other. Nonlinearity of
PMSM and existence of inverter in its control circuit caused that the
system is nonlinear and time-variant. With deriving average model,
the system is changed to nonlinear time-invariant and then the
nonlinear system is converted to linear system by linearization of
model around average values. This model is used to optimize vector
control then two optimal vector controls are compared to each other.
Simulation results show that the performance and robustness to noise
of the control system has been highly improved.
Abstract: Artifact free photoplethysmographic (PPG) signals are
necessary for non-invasive estimation of oxygen saturation (SpO2) in
arterial blood. Movement of a patient corrupts the PPGs with motion
artifacts, resulting in large errors in the computation of Sp02. This
paper presents a study on using Kalman Filter in an innovative way
by modeling both the Artillery Blood Pressure (ABP) and the
unwanted signal, additive motion artifact, to reduce motion artifacts
from corrupted PPG signals. Simulation results show acceptable
performance regarding LMS and variable step LMS, thus
establishing the efficacy of the proposed method.
Abstract: With the advent of inexpensive 32 bit floating point digital signal processor-s availability in market, many computationally intensive algorithms such as Kalman filter becomes feasible to implement in real time. Dynamic simulation of a self excited DC motor using second order state variable model and implementation of Kalman Filter in a floating point DSP TMS320C6713 is presented in this paper with an objective to introduce and implement such an algorithm, for beginners. A fractional hp DC motor is simulated in both Matlab® and DSP and the results are included. A step by step approach for simulation of DC motor in Matlab® and “C" routines in CC Studio® is also given. CC studio® project file details and environmental setting requirements are addressed. This tutorial can be used with 6713 DSK, which is based on floating point DSP and CC Studio either in hardware mode or in simulation mode.
Abstract: The innovative fuzzy estimator is used to estimate the
ground motion acceleration of the retaining structure in this study. The
Kalman filter without the input term and the fuzzy weighting recursive
least square estimator are two main portions of this method. The
innovation vector can be produced by the Kalman filter, and be
applied to the fuzzy weighting recursive least square estimator to
estimate the acceleration input over time. The excellent performance
of this estimator is demonstrated by comparing it with the use of
difference weighting function, the distinct levels of the measurement
noise covariance and the initial process noise covariance. The
availability and the precision of the proposed method proposed in this
study can be verified by comparing the actual value and the one
obtained by numerical simulation.
Abstract: This paper tests the level of market integration between Malaysia and Singapore stock markets with the world market. Kalman Filter (KF) methodology is used on the International Capital Asset Pricing Model (ICAPM) and the pricing errors estimated within the framework of ICAPM are used as a measure of market integration or segmentation. The advantage of the KF technique is that it allows for time-varying coefficients in estimating ICAPM and hence able to capture the varying degree of market integration. Empirical results show clear evidence of varying degree of market integration for both case of Malaysia and Singapore. Furthermore, the results show that the changes in the level of market integration are found to coincide with certain economic events that have taken placed. The findings certainly provide evidence on the practicability of the KF technique to estimate stock markets integration. In the comparison between Malaysia and Singapore stock market, the result shows that the trends of the market integration indices for Malaysia and Singapore look similar through time but the magnitude is notably different with the Malaysia stock market showing greater degree of market integration. Finally, significant evidence of varying degree of market integration shows the inappropriate use of OLS in estimating the level of market integration.
Abstract: Robust nonlinear integrated navigation of GPS and
low cost MEMS is a hot topic of research these days. A robust filter
is required to cope up with the problem of unpredictable
discontinuities and colored noises associated with low cost sensors.
H∞ filter is previously used in Extended Kalman filter and Unscented
Kalman filter frame. Unscented Kalman filter has a problem of
Cholesky matrix factorization at each step which is a very unstable
operation. To avoid this problem in this research H∞ filter is
designed in Square root Unscented filter framework and found 50%
more robust towards increased level of colored noises.
Abstract: In this paper, we consider the problem of tracking
multiple maneuvering targets using switching multiple target motion
models. With this paper, we aim to contribute in solving the problem
of model-based body motion estimation by using data coming from
visual sensors. The Interacting Multiple Model (IMM) algorithm is
specially designed to track accurately targets whose state and/or
measurement (assumed to be linear) models changes during motion
transition. However, when these models are nonlinear, the IMM
algorithm must be modified in order to guarantee an accurate track.
In this paper we propose to avoid the Extended Kalman filter because
of its limitations and substitute it with the Unscented Kalman filter
which seems to be more efficient especially according to the
simulation results obtained with the nonlinear IMM algorithm (IMMUKF).
To resolve the problem of data association, the JPDA
approach is combined with the IMM-UKF algorithm, the derived
algorithm is noted JPDA-IMM-UKF.
Abstract: This paper describes the two stage control using a disturbance observer and a Kalman filter. The system feedback uses the estimated state when it controls the speed. After the change-over point, its feedback uses the controlled plant output when it controls the position. To change the system continually, a change-over point has to be determined pertinently, and the controlled plant input has to be adjusted by the addition of the appropriate value. The proposed method has noise-reduction effect. It changes the system continually, even if the controlled plant identification has the error. Although the conventional method needs a speed sensor, the proposed method does not need it. The proposed method has a superior robustness compared with the conventional two stage control.
Abstract: The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: A new observer based fault detection and diagnosis
scheme for predicting induction motors- faults is proposed in this
paper. Prediction of incipient faults, using different variants of
Kalman filter and their relative performance are evaluated. Only soft
faults are considered for this work. The data generation, filter
convergence issues, hypothesis testing and residue estimates are
addressed. Simulink model is used for data generation and various
types of faults are considered. A comparative assessment of the
estimates of different observers associated with these faults is
included.
Abstract: In the normal operation conditions of a pico satellite,
conventional Unscented Kalman Filter (UKF) gives sufficiently good
estimation results. However, if the measurements are not reliable
because of any kind of malfunction in the estimation system, UKF
gives inaccurate results and diverges by time. This study, introduces
Robust Unscented Kalman Filter (RUKF) algorithms with the filter
gain correction for the case of measurement malfunctions. By the use
of defined variables named as measurement noise scale factor, the
faulty measurements are taken into the consideration with a small
weight and the estimations are corrected without affecting the
characteristic of the accurate ones. Two different RUKF algorithms,
one with single scale factor and one with multiple scale factors, are
proposed and applied for the attitude estimation process of a pico
satellite. The results of these algorithms are compared for different
types of measurement faults in different estimation scenarios and
recommendations about their applications are given.
Abstract: Flight management system (FMS) is a specialized
computer system that automates a wide variety of in-flight tasks,
reducing the workload on the flight crew to the point that modern
aircraft no longer carry flight engineers or navigators. The primary
function of FMS is to perform the in-flight management of the flight
plan using various sensors (such as GPS and INS often backed up by
radio navigation) to determine the aircraft's position. From the
cockpit FMS is normally controlled through a Control Display Unit
(CDU) which incorporates a small screen and keyboard or touch
screen. This paper investigates the performance of GPS/ INS
integration techniques in which the data fusion process is done using
Kalman filtering. This will include the importance of sensors
calibration as well as the alignment of the strap down inertial
navigation system. The limitations of the inertial navigation systems
are investigated in order to understand why INS sometimes is
integrated with other navigation aids and not just operating in standalone
mode. Finally, both the loosely coupled and tightly coupled
configurations are analyzed for several types of situations and
operational conditions.
Abstract: We consider optimal channel equalization for MIMO
(multi-input/multi-output) time-varying channels in the sense of
MMSE (minimum mean-squared-error), where the observation noise
can be non-stationary. We show that all ZF (zero-forcing) receivers
can be parameterized in an affine form which eliminates completely
the ISI (inter-symbol-interference), and optimal channel equalizers
can be designed through minimization of the MSE (mean-squarederror)
between the detected signals and the transmitted signals,
among all ZF receivers. We demonstrate that the optimal channel
equalizer is a modified Kalman filter, and show that under the AWGN
(additive white Gaussian noise) assumption, the proposed optimal
channel equalizer minimizes the BER (bit error rate) among all
possible ZF receivers. Our results are applicable to optimal channel
equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers,
OFDM (orthogonal frequency division multiplexing),
and DS (direct sequence) CDMA (code division multiple access)
wireless data communication systems. A design algorithm for optimal
channel equalization is developed, and several simulation examples
are worked out to illustrate the proposed design algorithm.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.
Abstract: Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Abstract: This paper presents an algorithm for the recognition
and tracking of moving objects, 1/10 scale model car is used to verify
performance of the algorithm. Presented algorithm for the recognition
and tracking of moving objects in the paper is as follows. SURF
algorithm is merged with Lucas-Kanade algorithm. SURF algorithm
has strong performance on contrast, size, rotation changes and it
recognizes objects but it is slow due to many computational
complexities. Processing speed of Lucas-Kanade algorithm is fast but
the recognition of objects is impossible. Its optical flow compares the
previous and current frames so that can track the movement of a pixel.
The fusion algorithm is created in order to solve problems which
occurred using the Kalman Filter to estimate the position and the
accumulated error compensation algorithm was implemented. Kalman
filter is used to create presented algorithm to complement problems
that is occurred when fusion two algorithms. Kalman filter is used to
estimate next location, compensate for the accumulated error. The
resolution of the camera (Vision Sensor) is fixed to be 640x480. To
verify the performance of the fusion algorithm, test is compared to
SURF algorithm under three situations, driving straight, curve, and
recognizing cars behind the obstacles. Situation similar to the actual is
possible using a model vehicle. Proposed fusion algorithm showed
superior performance and accuracy than the existing object
recognition and tracking algorithms. We will improve the performance
of the algorithm, so that you can experiment with the images of the
actual road environment.
Abstract: In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.