Caglar Uyulan

Comparison of the re-adhesion control strategies in high-speed train

Excessive driving force applied to the trains leads to an inadequate utilization of the adhesion phenomenon occurred at the wheel–rail contact, and an unnecessary power consumption, while inadequate driving force causes the train to run inefficiently. For this reason, the necessity of re-adhesion control in the safe and reliable operation, in the balance of energy consumption, is indisputable. A comparison of the two re-adhesion control strategies, one of which is robust adaptive and the other of which is the modified super-twisting sliding mode, has been presented in this article. These control algorithms developed suppress the wheel slip on time and maintain optimal traction performance after re-adhesion under the nonlinear properties of the traction system and the uncertainties of the adhesion level at the wheel–rail interface. Due to the complex nonlinear relationship between the adhesion force and the slip angular velocity, such a problem becomes a hard problem to overcome as long as the optimal slip ratio is not known. An optimal search strategy has also been developed to estimate and to track the desired slip angular velocity. By means of the proposed strategies, the traction motor control torque is automatically adjusted so as to ensure that the train operates away from the unstable slip zone but adjacent to the optimal adhesion region, and the desired traction capability is attainable once adhesion is regained. Mathematical analyzes are also provided to ensure the ultimate boundedness of the algorithms developed. The effectiveness of the proposed re-adhesion strategies is validated through the theoretical analysis and numerical simulations conducted in MATLAB and Simulink. As a result of consecutive simulations, modified super-twisting algorithm has shown better performance as compared to the robust adaptive one in tracking the optimal slip velocity as wheel–rail contact conditions switch suddenly.

Nonlinear Dynamic Characteristics of the Railway Vehicle

Abstract The nonlinear dynamic characteristics of a railway vehicle are checked into thoroughly by applying two different wheel-rail contact model: a heuristic nonlinear friction creepage model derived by using Kalker ’s theory and Polach model including dead-zone clearance. This two models are matched with the quasi-static form of the LuGre model to obtain more realistic wheel-rail contact model. LuGre model parameters are determined using nonlinear optimization method, which it’s objective is to minimize the error between the output of the Polach and Kalker model and quasi-static LuGre model for specific operating conditions. The symmetric/asymmetric bifurcation attitude and stable/unstable motion of the railway vehicle in the presence of nonlinearities which are yaw damping forces in the longitudinal suspension system are analyzed in great detail by changing the vehicle speed. Phase portraits of the lateral displacement of the leading wheelset of the railway vehicle are drawn below and on the critical speeds, where sub-critical Hopf bifurcation take place, for two wheel-rail contact model. Asymmetric periodic motions have been observed during the simulation in the lateral displacement of the wheelset under different vehicle speed range. The coexistence of multiple steady states cause bounces in the amplitude of vibrations, resulting instability problems of the railway vehicle. By using Lyapunov’s indirect method, the critical hunting speeds are calculated with respect to the radius of the curved track parameter changes. Hunting, which is defined as the oscillation of the lateral displacement of wheelset with a large domain, is described by a limit cycle-type oscillation nature. The evaluated accuracy of the LuGre model adopted from Kalker’s model results for prediction of critical speed is higher than the results of the LuGre model adopted from Polach’s model. From the results of the analysis, the critical hunting speed must be resolved by investigating the track tests under various kind of excitations.

Analysis of Time - Frequency EEG Feature Extraction Methods for Mental Task Classification

Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG) signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close) and performing mathematical operations), discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD), Morlet Wavelet Transform (MWT), Short Time Fourier Transform (STFT) and Wavelet Filter Bank (WFB), respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN) trained with back propagation (BP) algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT) conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different

Modeling, simulation and re-adhesion control of an induction motor–based railway electric traction system

Increasing the traction force is a complex problem in the design of railway vehicles; therefore, effective traction systems and algorithms have to be developed. During the traction process, the verification of traction algorithms and control strategies are based on simulations covering all locomotive dynamics. In this article, traction model of a railway vehicle and re-adhesion control method based on simulation approach are investigated to obtain more effective results. The longitudinal dynamic of a railway vehicle having traction system, which comprises two parallel motor groups, each of which has two field-oriented induction motor connected in series, is simulated to examine time-dependent changes in motor stator currents, traction torque, adhesion and resistance forces according to a given speed reference. The interaction between the adhesion force and the slip ratio is established according to the Burckhardt adhesion model, and a modified super-twisting sliding mode slip control is implemented in a computer simulation under various contact conditions so that simulation results approve the presented control method works under the maximum adhesion force. The comparison between the classical and modified version of the proposed control strategy was made to better evaluate the performance of the control system and to better optimize the traction system.

Stability and bifurcation analysis of the non-linear railway bogie dynamics

It is a critical issue to maintain stability in high-speed railway vehicles and to ensure comfortable and safe driving. Multi-body models of railway vehicles have non-linear properties originated from the wheel–rail contact and characteristics of the suspension systems. The critical speed values at which the unstable oscillations and the amplitudes of the limit cycle-type vibrations take place vary by adjusting the design parameters; therefore, these effects on non-linear railway dynamics must be evaluated with a higher precision by using numerical and/or analytical methods to determine the bifurcation behavior. The main objective of this paper is to examine the non-linear phenomena in a railway bogie from a broad perspective, concentrating on non-linear analysis methods. Thus, non-linear equations of motion of a 12-degrees of freedom railway bogie involving dual wheelsets, non-linear wheel flange contact, heuristic non-linear creep model, and suspension system are solved in the time domain with small time steps by using ode23s (stiff/Mod.Rosenbrock) method. The critical speeds were calculated with respect to the effects of various lateral stiffness and damping coefficients. The bifurcation diagrams of the maximum lateral displacement of the leading wheelset were depicted within a wide speed range. In the case of the suspension parameter set where the subcritical/supercritical Hopf bifurcation takes place, the phase portraits and the symmetric/asymmetric oscillations of the leading wheelset at the critical speed were represented. The type of the Hopf bifurcation can be transformed from the subcritical state to the supercritical state by increasing the given suspension ratio. The Lyapunov exponents of the lateral displacement, lateral velocity, yaw angular displacement, and yaw angular velocity of the leading wheelset were evaluated above the critical speeds to examine chaotic motion. The effect of the suspension parameters on the non-linear dynamical behavior of the railway bogie at the stability limit and on the bifurcation type has been proved.

Dynamic Investigation of the Hunting Motion of a Railway Bogie in a Curved Track via Bifurcation Analysis

The main purpose of this paper is to analyze and compare the Hopf bifurcation behavior of a two-axle railway bogie and a dual wheelset in the presence of nonlinearities, which are yaw damping forces in the longitudinal suspension system and heuristic creep model of the wheel-rail contact including dead-zone clearance, while running on a curved track. Two-axle railway bogie and dual wheelset were modeled using 12-DOF and 8-DOF system with considering lateral, vertical, roll, and yaw motions. By utilizing Lyapunov’s indirect method, the critical hunting speeds related to these models are evaluated as track radius changes. Hunting defined as the lateral vibration of the wheelset with a large domain was characterized by a limit cycle-type oscillation behavior. Influence of the curved track radius on the lateral displacement of the leading wheelset was also investigated through 2D bifurcation diagram, which is employed in the design of a stable model. Frequency power spectra at critical speeds, which are related to the subcritical and supercritical bifurcations, were represented by comparing the two-axle bogie and dual wheelset model. The evaluated accuracy to predict the critical hunting speed is higher and the hunting frequency in unstable region is lower compared to the dual wheelset model.

Hunting stability and derailment analysis of the high speed railway vehicle moving on curved tracks

An advanced train model, which examines hunting instability and derailment in one integrated model implicitly is presented. In terms of a control subject, proposed model is compatible with nonlinear controllers to stabilise hunting oscillations and perform real-time derailment avoidance. The dynamical model, which consists of a vehicle body, two bogie frames, and two wheelsets in each bogie frame was modelled with 35-DOF. Heuristic nonlinear creep model and flange-rail contact model were used to reveal the effects of the creep forces and moments. The eigenvalues at the hunting speed were calculated by the assistance of the Gershgorin disc theorem. The vehicle speed influence on evaluated derailment quotient was investigated at a sharp radius of the curved track. Safe speeds were also estimated via active derailment criteria. The main superiority of the proposed model is that one can both predict incipient derailment actively and also determine nonlinear critical hunting speeds with higher precision.

Modeling, simulation and slip control of a railway vehicle integrated with traction power supply

Abstract Studies on the modeling and simulation of the railway vehicle traction system play an active role in the operation and planning phase of railway electrification. In this paper, the longitudinal dynamic of a light rail vehicle was modelled and simulated in Matlab-Simulink. The traction system consists of two parallel motor groups, each of which is composed of two seperately-excited motors connected in series. The first simulation scenario represents how the traction system works in acceleration and braking modes with respect to a given speed change profile. Within this scenario, the time dependent responses of the motor armature and excitation currents, fluxes, motor traction moment, adhesion, resistance forces and acceleration are evaluated, and the constant torque, field attenuation, operation zones and vehicle traction force curve are described. The second simulation scenario represents the slip control application, which examines the complex nonlinear relationship between the adhesion force and the slip ratio, were demonstrated. Modified super-twisting sliding mode slip control are performed under dry, wet and low wheel-rail contact conditions, which are sequentially switched. It has been confirmed by the simulation results that the proposed control strategy achieves the maximum adhesion force of the train. The main purposes of this study are to investigate the operation principles of the railway dynamics associated with acceleration or braking modes and to examine the effects of certain parameters related with the dynamical electromechanical traction system.

Comparison of Wavelet Families for Mental Task Classification

Wavelet theory is a widely used feature extraction method for raw electroencephalogram (EEG) signal processing. The nature of the EEG signal is non-stationary, therefore applying wavelet transform on EEG signals is a valuable process for extraction promising features. On the other hand, determining the proper wavelet family is a challenging step to get the best fitted features for high classification accuracy. In this paper, therefore, we focused on a comparative study of different Discrete Wavelet Transform (DWT) methods to find the most convenient wavelet function of wavelet families for a non-stationary EEG signal analysis to be used to classify mental tasks. For the classification process, four different mental tasks were selected to and we grouped each with another one to set dual tasked sets including all possible combinations. Feature extraction steps are performed using wavelet functions haar, coiflets (order 1), biorthogonal (order 6.8), reverse biorthogonal (order 6.8), daubechies (order 2) and, daubechies (order 4). Later, a specific feature reduction formula is applied to the extracted feature vector. Generated feature vector is then split into train and test data before the classification. Artificial neural network was used for classification of the extracted feature sets. From the result of the repeated analysis for each DWT methods, Coiflets performed relatively better compared to other wavelet families.