PSO Based EKF Wheel-rail Adhesion Estimation

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Havangi Ramazan; Moradi Maryam

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Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL ELECTRONICS, CONTROL AND OPTIMIZATION Year:2023 | Volume:6 | Issue:1 Page(s): 49-62

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Information Journal Paper

Title

PSO Based EKF Wheel-rail Adhesion Estimation

Author(s)

Havangi Ramazan | Moradi Maryam | Issue Writer Certificate

Keywords

Adhesion mode

wheel-rail

contact condition estimation

PSO based EKF

Abstract

An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through reduction of damaging processes in wheel-rail and optimum on-time operation. In order to overcome the problem of the wheel slip/slide at the wheel-rail contact surface, detection of adhesion and its changes has high importance and scientiﬁcally challenging, because adhesion is influenced by different factors. However, critical information this detection provides is applicable not only in the control of trains to avoid undesirable wear of the wheels/track but also the safety compromise of rail operations. The adhesion level between the wheel and rail cannot be measured directly but the friction on the rail surface can be measured using measurement techniques. Estimation of wheel-rail adhesion conditions during railway operations can characterize the braking and traction control system. This paper presents the particle swarm optimization (PSO) based Extended Kalman Filter (EKF) to estimate adhesion force. The main limitation in applying EKF to estimate states and parameters is that its optimality is critically dependent on the proper choice of the state and measurement noise covariance matrices. In order to overcome the mentioned difficulty, a new approach based on the use of the tuned EKF is proposed to estimate induction motor (as a main part of the train moving system) parameters. This approach consists of two steps: In the first step the covariance matrices are optimized by PSO and then, their values will be introduced in the estimation loop. .

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