A High-Order Fully Actuated System Approach for Prescribed Performance Tracking Control of Quadrotor Unmanned Aerial Vehicle With Time-Varying Uncertain Aerodynamic Parameters and Disturbances

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yang Chen, Dandan Zhang, Zhikai Zhang, Heng Zhang
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引用次数: 0

Abstract

In this paper, a high-order, fully actuated (HOFA) system approach-based adaptive control design, with guaranteed transient and steady-state tracking performances, is proposed for trajectory tracking of a lightweight, low-cost quadrotor unmanned aerial vehicle (QUAV). Unlike most existing results on adaptive control for QUAVs, we consider the aerodynamic parameters and disturbance terms of the QUAV model as time-varying uncertainties. These uncertainties are only assumed to be bounded, with no additional restrictions imposed on their bounds and derivatives, making our approach more suitable for QUAVs in dynamic harsh environments. Moreover, in contrast to the existing first-order state-space model-based methods such as typical adaptive backstepping designs, the proposed HOFA system-based approach does not require multiple design steps and can achieve arbitrarily assignable eigenstructure like a stabilized linear system, which makes the control design very simple and easy to implement for practical applications. In addition, by introducing the prescribed performance control technique and incorporating a novel appointed-time performance function, it is shown that all tracking errors can be steered into their predesignated precision regions within their respective pre-appointed settling times. Finally, the effectiveness and advantages of the proposed method are validated through simulation.

用于具有时变不确定气动参数和扰动的四旋翼无人机规定性能跟踪控制的高阶全驱动系统方法
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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