Adaptive neuro-fuzzy inference control for active stabilizer bars based on multiple data sources

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Science Progress Pub Date : 2024-09-10 DOI:10.1177/00368504241274976
Tuan Anh Nguyen
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Abstract

In this research, we propose using active stabilizer bars to prevent rollover when steering. An intelligent control solution, the adaptive neuro-fuzzy inference system (ANFIS), is established in this article to control the performance of the active anti-roll systems. In contrast to the previously published studies, the intelligent algorithm designed in this research has many outstanding advantages, such as generating a large impact force, a fast response time, a small phase difference, and high convergence ability. The data used to train ANFIS are carefully selected and combined from the previous studies. The initial simulation observes that the roll angle decreases significantly from 8.15° to 6.87° when the ANFIS algorithm is applied to regulate the anti-roll bars. In contrast, the roll angles for the proportional-integral-derivative (PID) and passive (Mechanical) situations are respectively recorded at 7.08° and 7.80°. The reduction of the vertical force at wheels is also solved when the ANFIS algorithm is applied instead of other methods. This value increases sharply from 671.06 N (without bars) to 3030.40 N (ANFIS control), while it only reaches 2544.27 N (PID control) and 1428.83 N (mechanical bars), according to the research findings. If the vehicle does not have the anti-roll bars, a rollover occurs in the second case when the vehicle steers at v3 (80 km/h) and v4 (90 km/h). In contrast, the interaction between the wheels and the road is well maintained when the automobile is equipped with active bars controlled by the ANFIS solution. This is demonstrated by the minimum vertical force value in the rear wheel, which reaches 2687.33 and 2447.33 N, respectively, for the abovementioned conditions. In general, the vehicle's rolling stability can be well guaranteed in all moving situations when using the ANFIS controller for the anti-roll system in the vehicle.
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基于多数据源的主动稳定杆自适应神经模糊推理控制
在这项研究中,我们建议使用主动稳定杆来防止转向时发生侧翻。本文建立了一种智能控制方案--自适应神经模糊推理系统(ANFIS),用于控制主动防侧倾系统的性能。与之前发表的研究相比,本研究设计的智能算法具有许多突出优势,如产生的冲击力大、响应时间快、相位差小、收敛能力强等。用于训练 ANFIS 的数据是从之前的研究中精心挑选和组合的。初步模拟观察到,当采用 ANFIS 算法调节防倾杆时,侧倾角从 8.15° 显著减小到 6.87°。相比之下,比例积分派生(PID)和被动(机械)情况下的侧倾角分别为 7.08°和 7.80°。当采用 ANFIS 算法而不是其他方法时,车轮垂直力的减少也得到了解决。根据研究结果,该值从 671.06 N(无防撞杆)急剧增加到 3030.40 N(ANFIS 控制),而仅达到 2544.27 N(PID 控制)和 1428.83 N(机械防撞杆)。如果车辆没有防倾杆,在第二种情况下,当车辆以 v3(80 km/h)和 v4(90 km/h)转向时,就会发生侧翻。与此相反,当汽车装有由 ANFIS 解决方案控制的主动防滚架时,车轮与路面之间的相互作用得以很好地保持。在上述条件下,后轮的最小垂直力值分别为 2687.33 牛顿和 2447.33 牛顿,就证明了这一点。总体而言,使用 ANFIS 控制器控制汽车防侧倾系统时,汽车在所有行驶情况下的滚动稳定性都能得到很好的保证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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