Intelligent Control of Active Suspension Systems
Intelligent Control of Active Suspension Systems
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DOI：
10.1109/tie.2010.2046581
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发表时间：
2011-02
影响因子：
7.7
通讯作者：
Jeen Lin;Ruey-Jing Lian
中科院分区：
文献类型：
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作者：
Jeen Lin;Ruey-Jing Lian
人们提出了一种自组织模糊控制器（SOFC）来控制工程应用。在控制过程中，SOFC以模糊规则的形式不断更新学习策略，从空模糊规则开始。这消除了为模糊逻辑控制器的设计寻找合适的隶属函数和模糊规则的问题。然而，在 SOFC 中为控制工程应用选择适当的参数（学习率和权重分布）是很困难的。为了解决 SOFC 引起的问题，本研究开发了一种混合自组织模糊和径向基函数神经网络控制器（HSFRBNC）。 HSFRBNC采用径向基函数神经网络（RBFN）来实时调节SOFC的这些参数，从而获得最优值，从而克服了SOFC的应用问题。为了确认所提出的 HSFRBNC 的适用性，将 HSFRBNC 应用于操纵主动悬架系统。然后对其控制性能进行评价。仿真结果表明，HSFRBNC在提高悬架系统使用寿命和汽车乘坐舒适性方面比SOFC具有更好的控制性能。
A self-organizing fuzzy controller (SOFC) has been proposed to control engineering applications. During the control process, the SOFC continually updates the learning strategy in the form of fuzzy rules, beginning with empty fuzzy rules. This eliminates the problem of finding appropriate membership functions and fuzzy rules for the design of a fuzzy logic controller. It is, however, arduous to select appropriate parameters (learning rate and weighting distribution) in the SOFC for control engineering applications. To solve the problem caused by the SOFC, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC). The HSFRBNC uses a radial basis-function neural-network (RBFN) to regulate in real time these parameters of the SOFC, so as to gain optimal values, thereby overcoming the problem of the SOFC application. To confirm the applicability of the proposed HSFRBNC, the HSFRBNC was applied in manipulating an active suspension system. Then, its control performance was evaluated. Simulation results demonstrated that the HSFRBNC offers better control performance than the SOFC in improving the service life of the suspension system and the ride comfort of a car.