A New Hybrid Model Predictive Controller Design for Adaptive Cruise of Autonomous Electric Vehicles

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 adaptive cruise. The modes are divided into following cruising, the MPC algorithm based simplified dual neural network (SDNN) proportional-integral-derivative (PID) single neuron (SN) applied mode cruising mode, respectively. SDNN used accelerate solution of quadratic programming (QP) problem proposed improve computation efficiency, while PID SN performs well in nonlinear time-varying conditions ACC system. Moreover, lateral dynamics control integrated designed system fulfill cruise curved road conditions. Furthermore, energy efficiency electric vehicle, feedback strategy proposed. simulation results show that effective both straight roads roads.