Explicit-time adaptive tracking control for aerial manipulator systems

doi:10.3785/j.issn.1008-973X.2025.09.020

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (9): 1964-1974 DOI: 10.3785/j.issn.1008-973X.2025.09.020

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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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

Received: 31 October 2024 Published: 25 August 2025

CLC:

TP 242

Fund: 四川省智能制造与机器人重大科技专项资助项目 (2019ZDZX0019).

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Cite this article:

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.

URL:

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

空中作业机器人系统显式时间自适应跟踪控制

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

Fig.1 Schematic diagram of aerial manipulator system structure

Fig.2 Control structure diagram of aerial manipulator system

Fig.3 Aerial manipulator system experimental platform

Fig.4 Fitting experimental results of motor speed and input PWM

Fig.5 Motor tension coefficient and torque coefficient fitting results

Fig.6 Estimation results of lumped uncertainty terms using adaptive neural network

Fig.7 Impact of disturbance compensation on system control performance

Fig.8 Numerical simulation results of attitude tracking control

Fig.9 Manipulator joint angle tracking results

Fig.10 Comparative experimental results of position tracking control

Fig.11 Quantitative results of position tracking simulation experiment

Fig.12 Performance comparison of different convergence times in explicit time control

Fig.13 Attitude tracking experimental device of aerial manipulator system

Fig.14 Comparative results of aerial manipulator flight experiments using different control methods

Tab.1 Quantitative comparison of different control methods in flight experiments

Fig.15 Disturbance-rejection flight experiments of aerial manipulator

Fig.16 Comparative experimental results of disturbance rejection of aerial manipulator

Tab.2 Quantitative comparison results of disturbance-rejection performance of different controll method


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