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ADAPTIVE FUZZY CONTROLLER: APPROXIMATION ERROR ESTIMATOR APPROACH

机译:自适应模糊控制器:近似误差估计器方法

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Stable adaptive fuzzy control can be defined as a self-tuning concept for fuzzy controllers that guarantees stability of all variables in the system and convergence of the plant output to a given reference signal. In this paper, an adaptive fuzzy controller using approximation error estimator approach is proposed. The controller consists of 2 Takagi-Sugeno fuzzy systems, which are used to learn the non-linear dynamics of the plant. Moreover, the errors caused by approximating the real plant by fuzzy systems are also estimated on-line. It is shown that the proposed controller ensures the uniform ultimate boundedness of the close-loop systems. It is also shown that the inclusion of approximation error estimators improves the performance of the controller and reduces the on-line computation time. Finally, simulation examples are presented to demonstrate the approach.
机译:稳定的自适应模糊控制可以定义为模糊控制器的自整定概念,它可以保证系统中所有变量的稳定性以及将工厂输出收敛到给定参考信号的能力。本文提出了一种采用近似误差估计器的自适应模糊控制器。该控制器由2个Takagi-Sugeno模糊系统组成,用于学习设备的非线性动力学。此外,还可以在线估计由于用模糊系统逼近真实植物而引起的误差。结果表明,所提出的控制器确保了闭环系统的统一极限有界性。还表明,包含近似误差估计器可改善控制器的性能并减少在线计算时间。最后,给出了仿真实例来说明该方法。

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