The tracking accuracy of speed plays a significant role in the autonomous vehicle's control and safety management. In this study, we presented a novel method called self‐adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN‐PID) which is shown with improved longitudinal speed tracking accuracy for autonomous vehicles. A forward simulation model of longitudinal speed control for autonomous vehicles is established based on the driver model of self‐adaptive RBFNN‐PID and the vehicle dynamics model. Based on that, we used the traditional PID and fuzzy control methods as benchmarks to demonstrate the edge of the self‐adaptive RBFNN‐PID control under the new European driving cycle. Simulation results show the RBFNN‐PID method is significantly more precise than the comparing groups, with a reduced error in the range of [−0.369, 0.203] m/s. The vehicle performance gives better ride comfort as well. In all, self‐adaptive RBFNN‐PID is proven to be effective in longitudinal speed control of autonomous vehicles and significantly outperforms the other two methods.
Longitudinal speed control of autonomous vehicle based on a self‐adaptive PID of radial basis function neural network
IET Intelligent Transport Systems ; 12 , 6 ; 485-494
2018-08-01
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
fuzzy control , fuzzy control methods , self‐adaptive PID , forward simulation model , radial basis function networks , longitudinal speed control , autonomous vehicle control , road safety , ride comfort , radial basis function neural network , longitudinal speed tracking accuracy , European driving cycle , road traffic control , neurocontrollers , automobiles , self‐adaptive proportional integral derivative control , self‐adaptive RBFNN‐PID driver model , adaptive control , vehicle dynamics , safety management , velocity control , vehicle dynamics model
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