非线性自适应滑模控制及其在四轴飞行器中的应用

IF 2.4 Q2 ENGINEERING, MECHANICAL
Ryan Mathewson, F. Fahimi
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引用次数: 1

摘要

非线性自适应滑模控制(NASMC)具有对控制器设计者不知道参数的系统进行充分控制的能力。传统的基于模型的控制器要求系统具有已知参数的数学动态模型。这些系统参数通常是通过大量的系统识别实验来确定的,这些实验既昂贵又耗时。提出了一种不需要已知系统参数的NASMC方法。使用NASMC,控制器设计人员可以跳过昂贵且耗时的系统参数识别,并快速进入控制实现。此外,一旦使用NASMC为四轴飞行器导出了控制器,同一控制器将适用于具有相同运动方程但不同动态参数的任何四轴飞行器。给出了一般二阶和四阶系统的NASMC公式。然后,作为一个实现实例,将通用NASMC方法应用于四轴飞行器的仿真,验证了该方法的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear adaptive sliding mode control with application to quadcopters
Abstract Nonlinear adaptive sliding mode control (NASMC) has the capability to adequately control a system whose parameters are unknown to the controller designer. Conventional model-based controllers require a mathematical dynamic model of the system with known parameters. These system parameters are normally determined by extensive system identification experiments, which are expensive and time-consuming to perform. A NASMC approach that does not require known system parameters is proposed. Using NASMC, a controller designer can skip the expensive and time-consuming system parameter identification and fast-forward to the control implementation. In addition, once a controller is derived for a quadcopter using NASMC, the same controller will work on any quadcopter with the same equations of motion but different dynamic parameters. The formulation of the NASMC is presented for general second-order and fourth-order systems. Then, as an implementation example, the application of the general NASMC approach is demonstrated by applying it to a quadcopter unmanned aerial vehicle in simulation.
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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