Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System

Abstract

The vehicle dynamics model has multiple degrees of freedom, with strong nonlinear characteristics, so it is difficult to quickly obtain the accurate target oil pressure of an electronically assisted brake system based on the model. This paper proposes a target oil pressure recognition algorithm based on the T-S fuzzy neural network model. Firstly, the braking conditions classification algorithm is built according to the sampled braking intention data. The data are divided into the emergency braking condition data and the general braking condition data by the braking conditions classification algorithm. Secondly, the recognition model is trained respectively by the different braking condition data sets. In the training process, the fuzzy C-means clustering algorithm is used to identify the antecedent parameters of the model, and the learning rate cosine attenuation strategy is applied to optimize the parameter learning process. Finally, a correction method of target oil pressure based on slip ratio is proposed, and the target oil pressure derived following control methods based on traditional PID and fuzzy PID are compared through experiments. The results show that the mean square error of oil pressure following control based on fuzzy PID is smaller, which proves that the proposed method is able to precisely control braking force.

Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control

“…According to the braking conditions classification algorithm, the data were divided into the emergency braking condition data and the general braking condition data. The results show that fuzzy PID control could achieve precise control braking force [6]. Wang et al built a braking intention identification model using fuzzy c-means clustering algorithm.…”