PSO-Based EKF Wheel-Rail Adhesion Estimation

Abstract

An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through the reduction of damaging processes in wheel-rail and optimum on-time operation. To overcome the problem of the wheel slip/slide at the wheel-rail contact surface, it is crucial and scientifically challenging to detect adhesion and its changes 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 in 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. The braking and traction control system can be characterized by estimating wheel-rail adhesion conditions during railway operations. 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. To tackle this 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 are introduced into the estimation loop. Finally, the superiority of the PSO-based EKF algorithm is verified by making simulations in Matlab and comparing the estimation performances of this technique and EKF. The results prove an acceptable performance in load torque and speed estimation after tuning the covariance matrices and confirm the high accuracy and efficiency of the proposed method.