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基于输入饱和的欠驱动水面舰艇预定义时间跟踪控制     被引量:1

Predefined time tracking control of underactuated surface vessel with input saturation

文献类型:期刊文献

中文题名:基于输入饱和的欠驱动水面舰艇预定义时间跟踪控制

英文题名:Predefined time tracking control of underactuated surface vessel with input saturation

作者:黄秀颖[1];刘海涛[1,2];田雪虹[1,2]

机构:[1]广东海洋大学机械工程学院,广东湛江524088;[2]广东海洋大学深圳研究院,广东深圳518120

年份:2024

卷号:19

期号:1

起止页码:98

中文期刊名:中国舰船研究

外文期刊名:Chinese Journal of Ship Research

收录:北大核心2023、CSTPCD、、Scopus、CSCD_E2023_2024、北大核心、CSCD

基金:深圳市科技计划资助项目(JCYJ20220530162014033);广东省普通高校重点领域专项资助项目(2021ZDZX1041,2023ZDZX1005)。

语种:中文

中文关键词:欠驱动水面舰艇;预定义时间性能函数;障碍Lyapunov函数;自组织神经网络;输入饱和;预定义时间控制

外文关键词:underactuated surface vessel;predefined-time performance function;barrier Lyapunov function;self-structuring neural network;input saturation;predefined-time control

中文摘要:[目的]为解决欠驱动水面舰艇(USV)在模型不确定性、强耦合特性和控制器输入饱和情况下的轨迹跟踪问题,提出基于输入饱和的USV预定义时间跟踪控制方法。[方法]针对USV模型具有非零对角项和强耦合特性问题,引入坐标变换,将系统模型转变为斜对角形式;将预定义时间性能函数与障碍Lyapunov函数(BLF)结合,保证瞬态和稳态的跟踪性能;利用自组织神经网络(SSNN)降低未知外部环境扰动和复杂的连续未知非线性项以及输入饱和产生的影响,以保证控制系统的跟踪精度,并且在线调整优化SSNN的神经元数目,减少控制系统的计算负担。[结果]基于Lyapunov稳定性理论,证明了闭环系统在预定义时间内是有界稳定的,跟踪误差始终保持在约束范围内。[结论]仿真结果表明,所提控制策略是有效的,其具有良好的跟踪性能。

外文摘要:[Objective]To solve the trajectory tracking problem of underactuated surface vessels(USVs)under the condition of model uncertainty,strong coupling characteristics and controller input saturation,this study proposes a predefined time tracking control method for USVs based on input saturation.[Methods]Due to the non-zero diagonal terms and strong coupling characteristics of the USV model,coordinate transformation is introduced to transform the system model into a diagonal form.The predefined time performance function is combined with the barrier Lyapunov function(BLF)to ensure transient and stable tracking performance.Self-structuring neural networks(SSNN)are used to approximate unknown external disturbances and complex continuous unknown nonlinear terms,and deal with the impact of actuator saturation,thus ensuring the tracking performance of the control system.Moreover,the number of SSNN neurons can be adjusted online,reducing the computational burden on the control system.[Results]Based on Lyapunov stability theory,it is proven that the closed-loop system is bounded stable in a predefined time,and the tracking error is always within the constraint range.[Conclusion]The simulation results show that the proposed control strategy is effective and has good tracking performance.

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