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Disturbance-observer-based fuzzy terminal sliding mode control for MIMO uncertain nonlinear systems

机译:MIMO不确定非线性系统的基于扰动观测器的模糊终端滑模控制

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摘要

This study is concerned with the design of a disturbance-observer-based fuzzy terminal sliding mode controller (FTSMC) for multi-input multi-output (MIMO) uncertain nonlinear systems by considering unknown non-symmetric input saturation and control singularity. The disturbance observer is proposed for the unmeasured external disturbance and guarantees the convergence of the disturbance estimation error to zero in a finite time. The terminal sliding mode controller (TSMC) is designed for MIMO uncertain nonlinear systems by utilizing the output of the proposed disturbance observer. This control scheme combines the disturbance-observer-based TSMC with a fuzzy logic system in the presence of unknown non-symmetric input saturation and control singularity in order to reduce chattering phenomena. Finite time asymptotic stability, convergence of the disturbance observer, and convergence of the closed-loop system are proved via Lyapunov stability theorem. In addition, a five-rotor unmanned aerial vehicle (UAV) is employed in the numerical simulations to demonstrate the effectiveness and performance of the proposed control scheme. Disturbance observer estimates the payload and flight endurance of the five-rotor UAV. Genetic algorithm (GA) optimization is used to specify the parameters of the disturbance-observer-based TSMC (GATSMC) to decrease chattering. Finally, the superior performance of FTSMC is investigated over TSMC and GATSMC. (C) 2019 Elsevier Inc. All rights reserved.
机译:这项研究与多输入多输出(MIMO)不确定非线性系统的基于干扰观察者的模糊终端滑模控制器(FTSMC)的设计有关,该设计考虑了未知的非对称输入饱和度和控制奇异性。提出了一种针对未测外部干扰的干扰观测器,可以保证干扰估计误差在一定时间内收敛到零。终端滑模控制器(TSMC)通过利用提出的干扰观测器的输出设计用于MIMO不确定非线性系统。该控制方案在未知的非对称输入饱和度和控制奇异性的情况下,将基于干扰观测器的台积电与模糊逻辑系统结合在一起,以减少抖动现象。通过Lyapunov稳定性定理证明了有限时间渐近稳定性,扰动观测器的收敛性以及闭环系统的收敛性。此外,在数值模拟中采用了五旋翼无人机(UAV),以证明所提出的控制方案的有效性和性能。干扰观察者估计五旋翼无人机的有效载荷和飞行耐久性。遗传算法(GA)优化用于指定基于干扰观察者的台积电(GATSMC)的参数,以减少抖动。最后,研究了FTSMC优于TSMC和GATSMC的性能。 (C)2019 Elsevier Inc.保留所有权利。

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