A fast algorithm to initialize cluster centroids in fuzzy clustering applications

The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In this paper, we propose the InoFrep, a novel data-dependent initialization algorithm for improving computational efficiency and robustness in prototype-based hard and fuzzy clustering. The InoFrep is a single-pass algorithm using the frequency polygon data of the feature with the highest peaks count in a dataset. By using the Fuzzy C-means (FCM) clustering algorithm, we empirically compare the performance of the InoFrep on one synthetic and six real datasets to those of two common initialization methods: Random sampling of data points and K-means++. Our results show that the InoFrep algorithm significantly reduces the number of iterations and the computing time required by the FCM algorithm. Additionally, it can be applied to multidimensional large datasets because of its shorter initialization time and independence from dimensionality due to working with only one feature with the highest number of peaks

Restricted structure non-linear generalized minimum variance control

This research presents the Restricted Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm for Linear Parameter-Varying (LPV) systems. The LPV systems are defined as linear plant subsystems within the control diagram and may include Non-linear (NL) input subsystems. The RS-NGMV control solution for the latter will be slightly different than the first one and have the capability of dealing with NL characteristics such as saturation, discontinuities and black-box terms. The controller is built in a low-order Restricted Structure (RS) in the form of a general z-transfer function. This brings forward two major advantages. First, it offers a high-order advanced control solution inside low-order control structures which are known for their natural robustness. Secondly, it is easier to operate and re-tune for the classically trained staff in the industry as it can be given the structures they are rather familiar with such as the PID. Another advantage of the RS-NGMV is its model-based design that enables a faster adaptation to implement different systems. Features of the RS-NGMV are investigated throughout the thesis with case studies from trends in engineering like robotics, autonomous and electric vehicles. The results show that the RS-NGMV is highly capable of adapting to set-point changes, parameter variations with its ability to update the control gains rapidly by using optimizations. Some extensions of algorithms have also been studied following recent directions in optimal/predictive control resulting in a new preview control approach and Scheduled RS-NGMV control.This research presents the Restricted Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm for Linear Parameter-Varying (LPV) systems. The LPV systems are defined as linear plant subsystems within the control diagram and may include Non-linear (NL) input subsystems. The RS-NGMV control solution for the latter will be slightly different than the first one and have the capability of dealing with NL characteristics such as saturation, discontinuities and black-box terms. The controller is built in a low-order Restricted Structure (RS) in the form of a general z-transfer function. This brings forward two major advantages. First, it offers a high-order advanced control solution inside low-order control structures which are known for their natural robustness. Secondly, it is easier to operate and re-tune for the classically trained staff in the industry as it can be given the structures they are rather familiar with such as the PID. Another advantage of the RS-NGMV is its model-based design that enables a faster adaptation to implement different systems. Features of the RS-NGMV are investigated throughout the thesis with case studies from trends in engineering like robotics, autonomous and electric vehicles. The results show that the RS-NGMV is highly capable of adapting to set-point changes, parameter variations with its ability to update the control gains rapidly by using optimizations. Some extensions of algorithms have also been studied following recent directions in optimal/predictive control resulting in a new preview control approach and Scheduled RS-NGMV control

Speed tracking of an electric vehicle using a restricted structure NGMV control algorithm

A Restricted-Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm is applied to a scalar quasi Linear Parameter-Varying (qLPV), or State-Dependent (SD), Electric Vehicle speed tracking control problem. The model represents the longitudinal vehicle dynamics with disturbance factors from the road and the environment such as road inclination, aerodynamic drag and the rolling resistance forces. The control problem is based on the longitudinal speed tracking under the impact of these disturbances with an emphasis on the inclination. The simulation studies consider constant speed, UDDS and HWFET drive cycle scenarios as the reference speed profiles. The Restricted-Structure (RS) controller is of low order and uses NGMV optimization to calculate the feedback gains. The results show that RS-NGMV is efficient in dealing with disturbances and parameter variations, and battery State of Charge (SOC) results are also presented

Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering

Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets

Robust Repetitive Controller for Fast AFM Imaging

Currently, Atomic Force Microscopy (AFM) is the most preferred Scanning Probe Microscopy (SPM) method due to its numerous advantages. However, increasing the scanning speed and reducing the interaction forces between the probe's tip and the sample surface are still the two main challenges in AFM. To meet these challenges, we take advantage of the fact that the lateral movements performed during an AFM scan is a repetitive motion and propose a Repetitive Controller (RC) for the z-axis movements of the piezo-scanner. The RC utilizes the profile of the previous scan line while scanning the current line to achieve a better scan performance. The results of the scanning experiments performed with our AFM set-up show that the proposed RC significantly outperforms a conventional PI controller that is typically used for the same task. The scan error and the average tapping forces are reduced by 66% and 58%, respectively when the scan speed is increased by 7-fold

SDRE preview control for a LPV modelled autonomous vehicle

This paper aims to present a Preview Controller by taking State-Dependent Riccati Equation (SDRE) approach to the standard Preview Control solution based on Linear Quadratic (LQ) framework. A Matlab/Simulink simulation study for performing a lane change task for a LPV modelled autonomous vehicle has been conducted. The control objective is to achievereference tracking under parameter variation. It is assumed that the system has access to the future reference information for Np preview steps. The results show that the SDRE Preview Controller demonstrates good transient behaviour and achieve reference tracking objective