基于自适应RBFNN噪声估计的自抗扰控制在姿态控制中的应用

doi:10.13973/j.cnki.robot.180019

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李希, 谭建豪. 基于自适应RBFNN噪声估计的自抗扰控制在姿态控制中的应用[J]. 机器人, 2019, 41(1): 9-18.DOI: 10.13973/j.cnki.robot.180019. LI Xi, TAN Jianhao. Application of the Active Disturbance Rejection Control Based on Adaptive RBFNN Noise Estimating to Attitude Control. ROBOT, 2019, 41(1): 9-18. DOI: 10.13973/j.cnki.robot.180019.

摘要针对旋翼飞行多关节机械臂内部参数不确定性、外部环境和自身机械臂规划运动对飞行平台的干扰问题设计了一种姿态控制方法.首先将跟踪微分器作为期望姿态角的过渡过程，利用自适应RBFNN（径向基函数神经网络）算法对旋翼飞行多关节机械臂内、外部干扰进行逼近估计并实时补偿.然后采用非线性状态误差反馈控制来实现旋翼飞行多关节机械臂的姿态跟踪控制，并利用李亚普诺夫函数进行稳定性分析.最后，在仿真平台上实现该算法，将其仿真结果分别与PID（比例-积分-微分）控制、传统自抗扰控制（ARDC）进行比较分析.并且在实际旋翼飞行多关节机械臂系统上进行了实验，在0.4s之内三轴姿态角可从0快速跟踪到0.6rad且无超调.该算法对各通道的扰动有较强的抗干扰性，对系统参数有较强的鲁棒性，并且明显优于ARDC和PID算法.结果说明该算法能有效地解决系统不确定性干扰问题以实现姿态角的准确、快速跟踪.

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	李希
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关键词 ：旋翼飞行多关节机械臂, 姿态控制, 干扰, 自适应RBFNN, 自抗扰控制

Abstract：An attitude control method is designed for the rotor-flying platform to reject disturbances from the uncertainty of the intrinsic parameters of RF-MJM (rotor-flying multi-joint manipulator), the external environment, and the force that the manipulator reacts on the platform during the motion planning. Firstly, the internal and external disturbances of RF-MJM are asymptotically estimated by adaptive RBFNN (radial basis function neural network) algorithm and compensated in real time, setting the tracking differentiator (TD) as transient process of the desired attitude. Then, attitude tracking control of RF-MJM is accomplished by using nonlinear state error feedback (NLSEF) control, and the stability is analyzed by Lyapunov function. Finally, the algorithm is implemented on the simulation platform and its result is analyzed through comparing with PID (proportional-integral-differential) control and traditional ADRC (active disturbance rejection control). And the algorithm is testified in the realistic system of RF-MJM, where the three-axis attitude angles can be rapidly tracked from 0 to 0.6rad in 0.4s without overshoot. The proposed algorithm significantly outperforms ADRC and PID algorithms, for it is of strong anti-jamming capacity against disturbances from different channels, and of better robustness to system parameters. Results show that the proposed algorithm can effectively resolve the problem of uncertain disturbances in system, and track the attitude rapidly and accurately.

Key words： rotor-flying multi-joint manipulator attitude control disturbance adaptive RBFNN (radial basis function neural network) ADRC (active disturbance rejection control)

收稿日期: 2018-01-09 录用日期: 2018-05-22

TP242.6

基金资助:国家自然科学基金（61433016）

通讯作者: 谭建豪,tanjianhao96@sina.com E-mail: tanjianhao96@sina.com

作者简介: 李希(1994-),女,硕士生.研究领域:机器人路径规划与运动控制
谭建豪(1962-),男,博士,教授.研究领域:人工智能与机器人系统,模式识别与自然计算,数据挖掘与系统辨识.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.180019 或 https://robot.sia.cn/CN/Y2019/V41/I1/9

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