Longitudinal Adaptive Control of Autonomous Vehicles Base on Optimization Algorithm

doi:10.16182/j.issn1004731x.joss.19-0372

Abstract

Abstract: Aiming at the nonlinear, time-varying and uncertain characteristics of the longitudinal motion of autonomous vehicles, a Radial Basis Function neural network(RBFNN) Proportional Integral Derivative(PID) controller base on Particle Swarm Optimization(PSO) is designed. A RBFNN is integrated into a PID controller so that parameters of the PID controller could be adjusted self-adaptively. To solve the problem that poor selection of initial parameters of RBFNN and PID might lead to overshoot or instability in the control system, PSO is adopted to optimize aforementioned initial parameters off-line. Finally, a closed-loop adaptive control system model is built in MATLAB/Simulink. Simulation results show that as compared to the traditional PID controller and a non-optimized RBFNN-PID controller, the proposed PSO-RBFNN-PID controller demonstrates a higher level of controlling accuracy and stability of speed control under the New European Driving Cycle (NEDC) test.

Key words: autonomous vehicle, longitudinal control, self-adaptive, PSO-RBFNN-PID controller

CLC Number:

TP391.9
U46

Yin Zhishuai, He Jiaxiong, Nie Linzhen, Guan Jiayi. Longitudinal Adaptive Control of Autonomous Vehicles Base on Optimization Algorithm[J]. Journal of System Simulation, 2021, 33(2): 409-420.

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