Optimal design of adaptive interval type-2 fuzzy sliding mode control using Genetic algorithm

The 2nd International Conference on Control, Instrumentation and Automation Pub Date : 2011-12-27 DOI:10.1109/ICCIAUTOM.2011.6356731

M. Ghaemi, M. Akbarzadeh-T., M. Jalaeian-F.

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引用次数: 9

Abstract

In this paper, a stable indirect adaptive interval type-2 fuzzy sliding mode control (AIT2-FSMC) is introduced for a class of nonlinear systems. In the presence of uncertainties, especially under noisy and external disturbances, interval type-2 fuzzy system can be helpful in approximating unknown nonlinear system functions. To achieve more efficiency, the proposed controller is designed to use the targeted combination of sliding mode as a robust controller, interval type-2 fuzzy system as a universal approximator, and adaptive control law as an online parameter's tuner. The interval type-2 adaptation law is derived using Lyapunov approach, and mathematical analysis proves the closed loop system to be asymptotically stable. Although stability of the controller is provided via Lyapunov approach, optimization is required for performance improvement. Genetic algorithm (GA), as a population-based approach, is then used to optimize the parameters of the interval Type-2 fuzzy sets. Simulation analysis shows that the optimized IT2FSMC can reach improved performance.

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基于遗传算法的自适应区间2型模糊滑模控制优化设计

针对一类非线性系统，提出了一种稳定的间接自适应区间2型模糊滑模控制(AIT2-FSMC)。在存在不确定性的情况下，特别是在有噪声和外界干扰的情况下，区间2型模糊系统有助于逼近未知的非线性系统函数。为了达到更高的控制效率，该控制器采用滑模作为鲁棒控制器，区间2型模糊系统作为通用逼近器，自适应控制律作为在线参数调谐器的目标组合。利用Lyapunov方法推导了区间型-2自适应律，并通过数学分析证明了闭环系统是渐近稳定的。虽然通过李雅普诺夫方法提供了控制器的稳定性，但性能改进需要优化。然后，采用基于种群的遗传算法对区间2型模糊集进行参数优化。仿真分析表明，优化后的IT2FSMC可以达到更高的性能。

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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The 2nd International Conference on Control, Instrumentation and Automation

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