控制理论(社会学)
制动器
助推器(火箭)
控制工程
人工神经网络
工程类
控制器(灌溉)
计算机科学
压力控制
汽车工程
控制(管理)
人工智能
机械工程
农学
生物
航空航天工程
作者
Yuan Ji,Junzhi Zhang,Chengkun He,Xiaohui Hou,Weilong Liu,Jinheng Han
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2022-09-05
卷期号:9 (1): 222-235
被引量:11
标识
DOI:10.1109/tte.2022.3204187
摘要
Brake-by-wire (BBW) is a fundamental function required by intelligent vehicles. And the One-Box electrohydraulic BBW (EHB) system is becoming the mainstream BBW solution. To improve the wheel pressure regulation (WPR) ability in the One-Box solution, this study proposes a new WPR scheme by directly coordinating the adjusting valves and the motor-powered booster instead of the traditional plunger pump. First, a disturbance rejection adaptive control (DRAC) method is proposed for the pressure control in the master cylinder during this special WPR process, which can deal with parameter uncertainty and disturbance simultaneously to maintain stable backpressure. Then, a novel adaptive neural network controller (ANNC) is constructed for the control of adjusting valves. The proposed ANNC consists of two radial basis function neural networks (RBFNNs) that can learn the system dynamics and open-loop control characteristics in real-time and require few computational resources. Finally, hardware-in-the-loop (HIL) experiments are conducted, and the results proved the superiority of the booster-based WPR scheme with the proposed control methods (DRAC + ANNC) compared with the method under the traditional pump-based WPR scheme by 19.8% and 31.6% in Multistep and Sine pressure tracking scenarios, respectively. This will further enhance the precise braking ability of BBW vehicles.
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