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access icon free Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network

  • Author(s): Linzhen Nie 1, 2, 3 ; Jiayi Guan 1, 2, 3 ; Chihua Lu 1, 2, 3 ; Hao Zheng 1, 2, 3 ; Zhishuai Yin 1, 2, 3
    • View affiliations
  • Source: Volume 12, Issue 6, August 2018, p. 485 – 494
    DOI:  10.1049/iet-its.2016.0293 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 08/11/2016, Accepted 20/01/2018, Revised 11/12/2017, Published 01/02/2018

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.

Inspec keywords: automobiles; adaptive control; vehicle dynamics; road traffic control; radial basis function networks; neurocontrollers; fuzzy control; velocity control; road safety

Other keywords: ride comfort; self-adaptive PID; forward simulation model; European driving cycle; self-adaptive RBFNN-PID driver model; longitudinal speed control; vehicle dynamics model; radial basis function neural network; safety management; longitudinal speed tracking accuracy; fuzzy control methods; autonomous vehicle control; self-adaptive proportional integral derivative control

Subjects: Road-traffic system control; Velocity, acceleration and rotation control; Fuzzy control; Self-adjusting control systems; Neurocontrol

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