Fuzzy-SMC Stabilization Control Based on Vehicle-Road Recognition for Real-time and Variable Speed Conditions (October 2024)

Distributed drive vehicles are able to control the output torque of each wheel independently, which can improve vehicle stability. The accuracy of road adhesion coefficient identification has a large impact on the results of yaw rate and side-slip angle estimation. A multivariate controller for real-time identification of road adhesion coefficients is designed for the Fuzzy-SMC (Fuzzy Sliding Mode Control) stability-vehicle speed coupling control of yaw stability and speed tracking of distributed drive electric vehicles. The Improved LM-MMSOFNN ( Levenberg Marquardt Multi-Module Self-Organizing Neural Networks) method is utilized to realize the road adhesion coefficient identification online, fuzzy sliding mode control is used to estimate the gain of the system to eliminate uncertainties and chattering. Hierarchical control method is adopted, the upper layer calculates the additional yaw moment with vehicle longitudinal speed tracking, and the lower layer is torque distribution layer. Considering motor characteristics and longitudinal force requirements, this control problem is transformed into a multi-objective optimization problem. In variable speed working condition, compared with SMC control Fuzzy-SMC control yaw rate curve is smoother, has less oscillation, the vehicle driving state is smoother. The value of side-slip angle is smaller the whole distance, and the maximum is 0.015deg. Thus it can be verified that the proposed method is adaptive to the real driving requirements.