GA-BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In-Wheel Motors

Abstract

The steering system is a key component of the unmanned driving electric vehicle with in-wheel motors (IWM-EV), which is closely related to the operating safety of the vehicle. To characterize the complex nonlinear structure of the steering system of unmanned driving IWM-EV, a hierarchical modeling and hybrid steering control approach are presented. Firstly the 2-DOF model is introduced for the entire vehicle system, and then the models of the steering system and the in-wheel drive system are analyzed sequentially. The steering torque control system based on electronic differential (ED) and differential assist steering (DAS) is studied. The back propagation neural network (BPNN) is used to optimize the network structure, parameters, and the weight coefficient of the hybrid steering system. The genetic algorithm (GA) is employed to optimize the initial weight of BPNN and search within a large range. The GA-BPNN model is established with the yaw moment and differential torque as the input of BPNN. Simulation and experimental results show that the proposed GA-BPNN-based hybrid steering control approach not only accelerates the convergence speed of steering torque weight adjustment but also improves the response speed and flexibility of the steering system. Through optimizing and distributing the steering torque dynamically, the proposed GA-BPNN-based control approach has inherited the advantages of both vehicle stability under ED and the steering assistance under DAS, which further guarantees the safety and stability of unmanned driving IWM-EV.