Finite-Time Observer Based Prescribed Performance Robust Control for Position Stabilization of Offshore Gangway Ends

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-12-30 DOI:10.1002/rnc.7792
Meng Li, Jialu Du
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Abstract

Consider an offshore gangway subject to mathematical model parameter uncertainties and disturbance uncertainties. A finite-time observer (FTO) is designed to estimate the motion states of the gangway end (GE) and the total uncertain nonlinear disturbances lumped by the parameter uncertainties and disturbance uncertainties. The bandwidth of the FTO is a function of the GE position estimation error and a prescribed performance function and is time-varying, and thus our designed FTO does not rely on the bounds of the derivatives of the total uncertain nonlinear disturbances compared with conventional FTOs. Whereby, according to the idea of the active disturbance rejection control, we develop a prescribed-performance robust control law (PPRCL) for GE position stabilization such that the position stabilization error can converge into a prescribed tolerance steady-state band in a finite settling time. Simulation results with comparisons show the robustness and the advantage of our developed PPRCL.

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基于有限时间观测器的海上舷梯末端位置稳定鲁棒控制
考虑一个受数学模型参数不确定性和干扰不确定性影响的海上舷梯。设计了一个有限时间观测器(FTO)来估计舷梯末端的运动状态和由参数不确定性和扰动不确定性集总的不确定非线性扰动。FTO的带宽是GE位置估计误差和规定性能函数的函数,并且是时变的,因此与传统的FTO相比,我们设计的FTO不依赖于总不确定非线性扰动的导数的边界。据此,根据自抗扰控制的思想,提出了一种通用电气位置镇定的规定性能鲁棒控制律(PPRCL),使位置镇定误差在有限的稳定时间内收敛到规定的容限稳态带内。仿真结果表明,该算法具有较好的鲁棒性和优越性。
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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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