A New Hybrid Model Predictive Controller Design for Adaptive Cruise of Autonomous Electric Vehicles
Yuanhang Chen, Guodong Feng, Shaofang Wu, Xiaojun Tan
Abstract
Autonomous driving is an appealing research topic for integrating advanced intelligent algorithms to transform automotive industries and human commuting. This paper focuses on a hybrid model predictive controller (MPC) design for an adaptive cruise. The driving modes are divided into following and cruising, and the MPC algorithm based on simplified dual neural network (SDNN) and proportional-integral-derivative (PID) based on single neuron (SN) are applied to the following mode and the cruising mode, respectively. SDNN is used to accelerate the solution of the quadratic programming (QP) problem of the proposed MPC algorithm to improve the computation efficiency, while PID based on SN performs well in the nonlinear and time-varying conditions in the ACC system. Moreover, lateral dynamics control is integrated into the designed system to fulfill cruise control in the curved road conditions. Furthermore, to improve the energy efficiency of the electric vehicle, an energy feedback strategy is proposed. The simulation results show that the proposed ACC system is effective on both straight roads and curved roads.