水下
控制理论(社会学)
补偿(心理学)
扭矩
扰动(地质)
PID控制器
人工神经网络
计算机科学
工程类
控制工程
控制(管理)
人工智能
物理
心理学
古生物学
海洋学
精神分析
地质学
温度控制
生物
热力学
作者
Dongyang Shang,Xiaopeng Li,Meng Yin,Shuai Zhou
标识
DOI:10.1016/j.oceaneng.2023.115245
摘要
The underwater flexible telescopic manipulator (UFTM) can be installed on a submersible to achieve underwater operation, which is conducive to the exploration and development of deep-sea resources. However, the motion of the UFTM is affected by underwater disturbance torque, which may lead to a decrease in operational accuracy. In addition, the flexible components in the UFTM, including flexible deformation and torsional deformation, will exacerbate fluctuations in the rotation angle underwater environment disturbance torque. In this paper, a control method using neural networks to identify underwater disturbance torques is proposed to improve the rotational tracking accuracy of the UFTM. Firstly, based on the assumed modal method (AMM) and Lagrange equation, the UFTM dynamic model considering two-dimensional deformation and underwater disturbance torque is derived. Then, based on the UFTM dynamic model and Lyapunov theorem, the adaptive law and control law of neural networks are designed. Finally, simulation and control experiments prove the effectiveness of the neural network compensation for underwater disturbance torque control strategy. Compared with the classical PID strategy and the sliding mode control, the control strategy of neural network compensation for underwater disturbance can effectively improve the tracking control accuracy of the UFTM.
科研通智能强力驱动
Strongly Powered by AbleSci AI