控制理论(社会学)
控制器(灌溉)
模糊逻辑
计算机科学
非线性系统
模糊控制系统
鲁棒控制
控制工程
控制系统
工程类
人工智能
控制(管理)
电气工程
物理
生物
量子力学
农学
作者
Ayad Al-Mahturi,Fendy Santoso,Matthew A. Garratt,Sreenatha G. Anavatti
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:52 (1): 655-666
被引量:11
标识
DOI:10.1109/tsmc.2020.3030078
摘要
Recently, Type-2 fuzzy systems have become increasingly prominent as they have been applied to various nonlinear control applications. This article presents an adaptive fuzzy controller based on the sliding-mode control theory. The proposed self-adaptive interval Type-2 fuzzy controller (SAF2C) is based on the Takagi–Sugeno (TS) fuzzy model and it accommodates the “enhanced iterative algorithm with stop condition” type-reducer, which is more computationally efficient than the “Kernel–Mendel” type-reduction algorithm. We developed an integrated multi-input–multi-output (MIMO) SAF2C-controller to reduce the computation time so that we can expedite the learning process of our control algorithm by 80% compared to separate single-input–single-output (SISO) controllers. The stability of our controller is proven using the Lyapunov technique. To ensure the applicability of the presented control scheme, we implemented our controller on various nonlinear systems, including a hexacopter unmanned aerial vehicle (UAV). We also compare the accuracy of our controller with a conventional proportional–integral–derivative autopilot system. Our research indicates around 20% improvement in its transient response, in addition to achieving a better noise rejection capability with respect to a Type-1 fuzzy counterpart.
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