Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network
Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network
- New search for: Nie, Linzhen
- New search for: Guan, Jiayi
- New search for: Lu, Chihua
- New search for: Zheng, Hao
- New search for: Yin, Zhishuai
- New search for: Nie, Linzhen
- New search for: Guan, Jiayi
- New search for: Lu, Chihua
- New search for: Zheng, Hao
- New search for: Yin, Zhishuai
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.
Document information
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
2018-03-20
10 pages
Article (Journal)
Electronic Resource
English
vehicle dynamics , radial basis function neural network , velocity control , road safety , ride comfort , longitudinal speed tracking accuracy , neurocontrollers , road traffic control , autonomous vehicle control , safety management , self-adaptive RBFNN-PID driver model , vehicle dynamics model , European driving cycle , self-adaptive proportional integral derivative control , fuzzy control , fuzzy control methods , radial basis function networks , longitudinal speed control , self-adaptive PID , automobiles , forward simulation model , adaptive control
Metadata by IET is licensed under CC BY 3.0
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