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http://dx.doi.org/10.7839/ksfc.2019.16.2.022

A Model Predictive Tracking Control Algorithm of Autonomous Truck Based on Object State Estimation Using Extended Kalman Filter  

Song, Taejun (Department of Mechanical Engineering, Hankyong National University)
Lee, Hyewon (Department of Mechanical Engineering, Hankyong National University)
Oh, Kwangseok (Department of Mechanical Engineering, Hankyong National University)
Publication Information
Journal of Drive and Control / v.16, no.2, 2019 , pp. 22-29 More about this Journal
Abstract
This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.
Keywords
Extended Kalman Filter; Model Predictive Control; Autonomous Truck; Tracking Control; 1-layer Laser Scanner;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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