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Premise-part adaptation laws for adaptive fuzzy control and its application to vehicle speed control

机译:适应性模糊控制及其在车速控制的应用的前提部分适配规律

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In the adaptive fuzzy control, approximation accuracy of the designed fuzzy system plays a key role in the overall system performance. Up to now, linear parameterization method has been used to derive suitable adaptive laws, even in the adaptation of premise-part membership functions. However, the premise-part adaptation schemes with linear parameterization have some fundamental limitation due to the inadequacy of gradient algorithm for general nonlinearly parameterized functions. In this paper, a new adaptive fuzzy control method with adaptation both of the premise-part and consequence-part membership functions is presented. The proposed adaptive fuzzy control scheme does not suffer from the problems appearing in conventional premise-part adaptation by using a nongradient strategy. The global stability as well as performance enhancement is given via simulations and application results to vehicle speed control.
机译:在自适应模糊控制中,设计的模糊系统的近似精度在整体系统性能中起着关键作用。到目前为止,即使在对前提部分隶属函数的适应范围内,也用于导出合适的自适应法律的线性参数化方法。然而,由于一般非线性参数化功能的梯度算法的不足,具有线性参数化的前提部分适配方案具有一些基本限制。本文提出了一种新的自适应模糊控制方法,适应适应的前提部分和后果部分隶属函数。所提出的自适应模糊控制方案不受违规策略在传统的前提部分适应中出现的问题。通过模拟和应用程序导致车辆速度控制给出全局稳定性以及性能增强。

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