用训练好的人工神经网络改进单单元重型车辆LQR主动防侧倾控制

Q4 Engineering

International Journal of Vehicle Safety Pub Date : 2016-10-10 DOI:10.1504/IJVS.2016.079657

S. Babesse, D. Ameddah

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引用次数: 0

摘要

针对具有线性和非线性侧力模型的单单元重型汽车悬架，采用神经网络改进了线性二次型调节器(LQR)主动防侧倾控制。首先，为了使归一化前后轴侧翻量等于或低于1，提出了LQR控制。之后，从该控制器收集的训练数据作为神经元调节器的训练基础。将人工神经网络控制器应用于非线性侧力模型，得到了比LQR更令人满意的结果。

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Improvement of LQR active anti-roll control of a single-unit heavy vehicle by means of a trained artificial neuronal network

In this paper, a neuronal network is used to improve a Linear-Quadratic Regulator (LQR) active anti-roll control applied to a single-unit heavy vehicle suspension with linear and non-linear side force model. First, to keep the normalised rollovers between front and rear axles, equal to or below unity, the LQR control is proposed. After that, the training data collected from this controller are used as a training basis of a neuronal regulator. The artificial neuronal network controller is thereafter applied for the non-linear side force model, and it gives more satisfactory results than the LQR.

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来源期刊

International Journal of Vehicle Safety Engineering-Automotive Engineering

CiteScore

0.30

自引率

0.00%

发文量

期刊介绍： The IJVS aims to provide a refereed and authoritative source of information in the field of vehicle safety design, research, and development. It serves applied scientists, engineers, policy makers and safety advocates with a platform to develop, promote, and coordinate the science, technology and practice of vehicle safety. IJVS also seeks to establish channels of communication between industry and academy, industry and government in the field of vehicle safety. IJVS is published quarterly. It covers the subjects of passive and active safety in road traffic as well as traffic related public health issues, from impact biomechanics to vehicle crashworthiness, and from crash avoidance to intelligent highway systems.