Nonlinear sliding mode predictive trajectory tracking control of underactuated marine vehicles: Theory and experiment

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-09-18 DOI:10.1002/rnc.7638

Run‐Zhi Wang, Li‐Ying Hao, Zhi‐Jie Wu

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引用次数: 0

Abstract

This article introduces a control method for trajectory tracking of underactuated unmanned marine vehicles (UMVs), employing the sliding mode predictive control (SMPC) scheme. To address the challenges of demonstrating system stability with a local feedback controller for underactuated UMVs in model predictive control (MPC), this article proposes an auxiliary controller design method based on sliding mode control. A sliding mode dynamic is derived through an error system and sliding surface equations. Compared to existing literature, which predominantly emphasizes demonstrating input‐state stability, this strategy ensures the asymptotic stability of the closed‐loop system by introducing a novel method for selecting weight matrices. Furthermore, extended terminal sets and feasible sets constructed via sliding variables are provided, thereby reducing conservatism. Ultimately, the SMPC scheme is validated through simulation and hardware experiments providing quantitative evidence of its effectiveness in real‐world applications.

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欠驱动海洋车辆的非线性滑模预测轨迹跟踪控制：理论与实验

本文介绍了一种采用滑模预测控制（SMPC）方案的控制方法，用于对欠动无人海洋航行器（UMV）进行轨迹跟踪。为了解决在模型预测控制（MPC）中使用局部反馈控制器来证明系统稳定性的难题，本文提出了一种基于滑模控制的辅助控制器设计方法。通过误差系统和滑动面方程推导出滑动模态动态。与主要强调证明输入状态稳定性的现有文献相比，该策略通过引入一种新的权重矩阵选择方法来确保闭环系统的渐近稳定性。此外，还提供了扩展的终端集和通过滑动变量构建的可行集，从而减少了保守性。最后，SMPC 方案通过仿真和硬件实验进行了验证，为其在实际应用中的有效性提供了量化证据。

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

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.