Adaptive sliding mode control for quadrotor transport systems with uncertain parameters and disturbances

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-08-13 DOI:10.1002/acs.3893
Longkang Huang, Yanhua Yang, Yang Chen
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引用次数: 0

Abstract

Quadrotor transportation systems have been widely utilized in both commercial and civilian fields. However, challenges arise due to the variable payload mass and unpredictable wind disturbances during cargo transport, potentially leading to excessive payload swinging and system instability. To tackle this issue, this article proposes an adaptive sliding mode control approach that concurrently achieves trajectory tracking for the quadrotor and mitigates payload swinging. In the outer loop subsystem, the quadrotor dynamics model is partitioned into two components that is, actuated and underactuated components. Sliding surfaces are designed based on this divided system model, and two adaptive laws are designed to compensate for payload mass uncertainty and unknown wind disturbances. The system's asymptotic stability is assured through the application of the Lyapunov theorem. In the inner loop subsystem, a disturbance rejection controller is formulated. Comparative simulation results conclusively demonstrate the outstanding performance of the proposed method in terms of trajectory tracking and robustness.

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具有不确定参数和干扰的四旋翼运输系统的自适应滑模控制
摘要四旋翼运输系统已广泛应用于商业和民用领域。然而,在货物运输过程中,由于有效载荷质量的可变性和不可预测的风力干扰,可能会导致有效载荷的过度摆动和系统的不稳定性,从而带来挑战。为解决这一问题,本文提出了一种自适应滑模控制方法,既能实现四旋翼飞行器的轨迹跟踪,又能减轻有效载荷的摆动。在外环子系统中,四旋翼飞行器动力学模型被分为两个部分,即致动部分和欠致动部分。滑动面的设计基于这个分割的系统模型,并设计了两个自适应法则来补偿有效载荷质量的不确定性和未知的风力干扰。通过应用 Lyapunov 定理,确保了系统的渐进稳定性。在内环子系统中,制定了干扰抑制控制器。比较仿真结果充分证明了所提方法在轨迹跟踪和鲁棒性方面的卓越性能。
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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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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