Power Systems
M. Moradi; R. Havangi
Abstract
The rail vehicle dynamics are greatly impacted by the different forces present at the contact region of wheel and rail. Since the wheel and rail adhesion force is the key element in maintaining high braking and acceleration performance of rail vehicles, continuous monitoring of this factor ...
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The rail vehicle dynamics are greatly impacted by the different forces present at the contact region of wheel and rail. Since the wheel and rail adhesion force is the key element in maintaining high braking and acceleration performance of rail vehicles, continuous monitoring of this factor is very important. Damage to the infrastructures and lack of transportation convenience are the most obvious consequences of improper levels of adhesion force. Adhesion modeling is a time consuming task. In addition, due to the difficulties exist in directly measuring the adhesion force, the use of alternative methods in which state observers are adopted has attracted much attention. The selection of the primary model of the studied system and the ability of the selected estimator have a significant effect on the success of the proposed approach in estimating the variables. In this paper, the dynamics of the wheelset is simulated in the presence of irregularities that can be encountered in the railroad. Estimation of wheel-rail adhesion force is done indirectly by nonlinear filters as estimators and their accuracies in the estimation are compared to identify the better one. Meanwhile, inertial sensors (accelerometer and gyroscope) outputs are used as measuring matrix and employed to simulate actual situation and evaluate the estimators performances. To check the accuracy and ability of the estimators in estimating states and variables, the proposed approache implemented in Matlab.This study introduces an advantageous approach that uses the longitudinal, lateral and torsional dynamics to estimate wheel-rail adhesion force in variable conditions. Experimental results showed high precision, fast convergence, and low error values in variable estimation. Real time knowledge of the contact condition results in proper traction and braking performances. The results of proposed method can lead to decreasing wheel deterioration and operational costs, minimizing high creep levels, maximizing the use of already-existing adhesion, and mproving the frequency of service. It is worth noting that the proposed method is beneficial for both conventional railway transport and automated driverless trains.
Power Electronics
M. Moradi; R. Havangi
Abstract
Background and Objectives: Traction system and adhesion between wheel and rail are fundamental aspects in rail transportation. Depending on the vehicle's running conditions, different levels of adhesion are needed. Low adhesion between wheel and rail can be caused by leaves on the line or other ...
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Background and Objectives: Traction system and adhesion between wheel and rail are fundamental aspects in rail transportation. Depending on the vehicle's running conditions, different levels of adhesion are needed. Low adhesion between wheel and rail can be caused by leaves on the line or other contaminants, such as rust or grease. Low adhesion can occur at any time of year especially in autumn, resulting in disruptions to passenger journeys. Increased wheel-rail adhesion for transit rail services results in better operating performance and system cost savings. Deceleration caused by low adhesion, will extend the braking distance, which is a safety issue. Because of many uncertain or even unknown factors, adhesion modelling is a time taking task. Furthermore, as direct measurement of adhesion force poses inherent challenges, state observers emerge as the most viable choice for employing indirect estimation techniques. Certain level of adhesion between wheel and rail leads to reliable, efficient, and economical operation.Methods: This study introduces an advantageous approach that leverages the behavior of traction motors to provide support in achieving control over wheel slip and adhesion in railway applications. The proposed method aims to enhance the utilization of existing adhesion, minimize wheel deterioration, and mitigate high creep levels. In this regard, estimation of wheel-rail adhesion force is done indirectly by concentrating on induction motor parameters as railway traction system and dynamic relationships. Meanwhile, in this study, we focus on developing and applying the sixth-order Extended Kalman Filter (EKF) to create a highly efficient sensorless re-adhesion control system for railway vehicles.Results: EKF based design is compared with Unscented Kalman Filter (UKF) based and actual conditions and implemented in Matlab to check the accuracy and performance ability for state and parameter estimation. Experimental results showed fast convergence, high precision and low error value for EKF.Conclusion: The proposed technique has the capability to identify and assess the current state of local adhesion, while also providing real-time predictions of wear. Besides, in combination with control methods, this approach can be very useful in achieving high wheel-rail adhesion performance under variable complex road conditions
Object Tracking
R. Havangi
Abstract
Background and Objectives:The target tracking problem is an essential component of many engineering applications.The extended Kalman filter (EKF) is one of the most well-known suboptimal filter to solve target tracking. However, since EKF uses the first-order terms of the Taylor series nonlinear extension ...
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Background and Objectives:The target tracking problem is an essential component of many engineering applications.The extended Kalman filter (EKF) is one of the most well-known suboptimal filter to solve target tracking. However, since EKF uses the first-order terms of the Taylor series nonlinear extension functions, it often makes large errors in the estimates of state. As a result, target tracking based on EKF may diverge. Methods: In this manuscript, an adaptive square root cubature Kalman filter (ASRCKF) is poposed to solve the maneuvering target tracking problem. In the proposed method, the covariance of process and measurement noises is estimated adaptively. Thus, the performance of proposed method does not depend on the noise statistics and its performance is robust with unknown prior knowledge of the noise statistics. Morover, it has a consistently improved numerical stability why the matrices of covariance are guaranteed to remain semi- positive. The performance of the proposed method is compared with EKF, and the unscented Kalman filter (UKF) for target tracking problem. Results:To evaluate the proposed method, many experiments is performed. The proposed method is evaluated on the non-maneuvering and maneuvering target tracking. Conclusion: The results show that the proposed method has lower estimation errors with faster convergence rate than other methods. The proposed method can track the tates of moving target effectively and improve the accuracy of the system.
