This study introduces an adaptive Fourier linear combiner (FLC) based on a modified least mean kurtosis (LMK) algorithm in order to effectively process sinusoidal signals, which we call FLC-LMK algorithm. In the design procedure of the proposed FLC-LMK algorithm, the classical kurtosis-based cost function is first modified for only sinusoidal signal distributions instead of Gaussian. Then, the FLC-LMK algorithm is derived from the minimization of this cost function and thus updates the weight coefficients of the FLC structure so as to directly process sinusoidal signals. Moreover, in this study, the convergence in the mean of the proposed FLC-LMK algorithm is analysed in order to determine the lower and upper bounds of its step size parameter. The most important contributions of the use of the proposed algorithm in the FLC structure are that it increases the convergence rate, decreases the steady-state error level and also has a robust behaviour against sinusoidal signal distributions due to its modified cost function. The performance of the proposed FLC-LMK algorithm is evaluated on the synthetic and real-world pathological hand tremor data by comparing with that of the FLC based on the classical least mean square (LMS) (FLC-LMS) algorithm. The simulation results support the mentioned properties of the proposed FLC-LMK algorithm.
A method of designing adaptive controller for high performance active magnetic bearings (AMB) is represented. The approach combines two simple architectures often referred to as a Dynamic Feedback Linearization (DFL) and recent advances in adaptive control design techniques to form a new approach for AMB. The procedure enables the designer to explicitly define the desired closed loop dynamics. The result is a straight forward procedure that enables the design of a robust stabilizing adaptive controller that forces the system dynamics to the specified desired dynamics, despite disturbances, modelling uncertainties, and variations in AMB dynamics. This method also guarantees the robustness of both stability and system performance and enables the design for other unstable, magnetic levitation plants.
Batterychargers are the energy transmission part of electric vehicles between the grid (or electric energy source) and the vehicle. This energy transmission process has a great effect on the technology of electric vehicles. Charging a vehicle as fast as possible is necessary for an electric vehicle to compete with the internal combustion engine vehicles. The technology has been dealing with this process near past, while charging a car was taking a few hours, this time is reducing day after day. In this paper it is aimed to give some information about the current technologies of chargers(only the chargers with cable not wireless chargers) and it is also aimed to design a trial Simulink model for chargers. When the literature is considered, simulation samples of chargers are little so increasing these samples is important to improve the technology offast charging.
Development of intelligent care system for elder people have been investigated in recent years. In this study, to detect emergency situations for elder people, activity classification was aimed using on body sensor data. Multi-layer perceptron, radial basis function networks, k- nearest neighbor and support vector machines were used in classification. In feature selection process principal component analysis and ReliefF were used. Accuracy of classification was above 85% for every classifier and the best performance was acquired with 3-NN with 99.8% accuracy. When feature selection was applied 5- NN was showed the highest performance with 99.4%. This study shows that it is possible to develop remote care system by using sensors and classifiers for a more secure life for elder people.
This paper studies the problem of stabilization for a class of linear singular time-delay systems. First, a delay-dependent stability criterion is developed within the context of Lyapunov stability theory and linear matrix inequalities. The stability result is then extended to obtain a stabilizing state-feedback controller within the bilinear matrix inequalities (BMI) framework. Employing the cone complementary linearization approach, the synthesis problem can be resolved through a set of LMI conditions. Several numerical examples are presented to illustrate the application of the theoretical results.
