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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (9): 1964-1974    DOI: 10.3785/j.issn.1008-973X.2025.09.020
机械工程     
空中作业机器人系统显式时间自适应跟踪控制
刘宜成(),马翔,严文
四川大学 电气工程学院，四川 成都 610065
Explicit-time adaptive tracking control for aerial manipulator systems
Yicheng LIU(),Xiang MA,Wen YAN
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
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摘要：

空中作业机器人系统的复杂物理结构使其在运行过程中易受复杂环境扰动影响，难以实现在较小控制输入下的固定时间稳定控制. 提出一种新型的显式时间自适应控制（ETAC）方法，在未知扰动存在时，使系统误差在显式时间内快速收敛. 利用牛顿-欧拉法建立空中作业机器人的动力学模型，设计自适应神经网络逼近策略，无须依赖先验知识即可估计扰动；结合显式时间稳定策略以确保控制收敛性，加快系统收敛速度，并有效缓解控制输入过大导致的控制器饱和问题. 数值仿真和飞行实验结果表明，与预定义时间控制方法相比，所提方法的控制输入减少了30.51%，系统误差收敛时间缩短了8.36%；在机械臂受到扰动的情况下，系统误差降低了31.25%. 该方法在保持较小控制输入的同时，显著增强了系统的抗扰动能力.
关键词： 空中作业机器人;  轨迹跟踪;  显式时间稳定策略;  扰动估计;  自适应神经网络    
Abstract:

A novel explicit-time adaptive control (ETAC) method was proposed to address the challenge of achieving fixed-time stable control with minimal control input in aerial manipulator systems which are susceptible to environmental disturbances during operation due to the complex physical structure. This method enabled rapid system error convergence within an explicit time frame even in the presence of unknown disturbances. The Newton-Euler method was utilized to establish the dynamic model of the aerial manipulator, and an adaptive neural network approximation strategy was designed to estimate disturbances without relying on prior knowledge. An explicit-time stability strategy was incorporated to ensure control convergence, accelerating system convergence while mitigating the problem of controller saturation caused by excessive control input. The results of numerical simulation and flight experiments indicated that, compared to the predefined-time control method, the proposed method reduced the control input by 30.51%, shortened the system error convergence time by 8.36%, and decreased the system error by 31.25% under manipulator disturbances. This method significantly enhances the system’s disturbance rejection capability while maintaining a lower control input.
Key words: aerial manipulator    trajectory tracking    explicit time stabilization strategy    disturbance estimation    adaptive neural networks
收稿日期: 2024-10-31 出版日期: 2025-08-25
CLC:  TP 242  
基金资助: 四川省智能制造与机器人重大科技专项资助项目 (2019ZDZX0019).
作者简介: 刘宜成（1975—），男，副教授，博士，从事机器人建模和控制研究. orcid.org/0000-0003-3571-3839. E-mail：liuyicheng@scu.edu.cn
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引用本文:

刘宜成,马翔,严文. 空中作业机器人系统显式时间自适应跟踪控制[J]. 浙江大学学报(工学版), 2025, 59(9): 1964-1974.

Yicheng LIU,Xiang MA,Wen YAN. Explicit-time adaptive tracking control for aerial manipulator systems. Journal of ZheJiang University (Engineering Science), 2025, 59(9): 1964-1974.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.09.020    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I9/1964

图 1  空中作业机器人系统结构示意图
图 2  空中作业机器人控制结构框图
图 3  空中作业机器人实验平台
图 4  电机转速和输入PWM拟合实验结果
图 5  电机拉力系数与转矩系数拟合结果
图 6  自适应神经网络对集总不确定项的估计结果
图 7  扰动补偿对系统控制性能的影响
图 8  姿态跟踪控制的数值仿真结果
图 9  机械臂关节角跟踪结果
图 10  位置跟踪控制对比实验结果
图 11  位置跟踪仿真实验量化结果
图 12  显式时间控制中不同收敛时间的性能对比
图 13  空中作业机器人姿态跟踪实验装置
图 14  不同控制方法的空中作业机器人飞行实验对比结果
姿态角控制方法${T_{\text{c}}}/{\mathrm{s}}$RMSE/(°)${E_{\max }}$/(°)
滚转角FTSMC1.760.659 21.883 8
PTSMC1.580.644 51.956 4
ETAC1.360.479 51.486 8
俯仰角FTSMC2.270.764 81.853 6
PTSMC1.850.523 11.611 8
ETAC1.800.397 21.096 4
偏航角FTSMC1.890.512 91.847 7
PTSMC1.350.384 11.161 8
ETAC1.220.256 10.923 9
表 1  飞行实验中不同控制方法的定量比较
图 15  空中作业机器人抗扰动飞行实验
图 16  空中作业机器人抗扰动对比实验结果
姿态角控制方法$\text { RMSE } /\left({ }^{\circ}\right) $$E_{\text {max }} /\left({ }^{\circ}\right) $
滚转角ADRC0.747 72.768 5
ETAC0.418 41.340 3
俯仰角ADRC0.754 83.875 4
ETAC0.588 03.012 3
偏航角ADRC0.285 91.388 4
ETAC0.222 91.062 1
表 2  不同控制方法抗扰性能定量比较结果
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