电子测量与仪器学报

孙非,曹宇赫,崔特,任超.基于权重自适应更新径向基函数神经网络的水下游动机械臂镇定控制[J].电子测量与仪器学报,2024,38(4):1-8

基于权重自适应更新径向基函数神经网络的水下游动机械臂镇定控制

Stabilization control of underwater swimming manipulator basedon radial basis function neural network compensatorwith adaptive weight updating law

DOI：

中文关键词: 水下游动机械臂动力学建模反馈线性化径向基函数神经网络

英文关键词:underwater swimming manipulator dynamic modeling feedback linearization radial basis function neural network

基金项目:国家自然科学基金（62073235）项目资助

作者	单位
孙非	天津大学内燃机研究所天津300072
曹宇赫	天津大学电气自动化与信息工程学院天津300072
崔特	天津大学电气自动化与信息工程学院天津300072
任超	天津大学电气自动化与信息工程学院天津300072

Author	Institution
Sun Fei	Tianjin Internal Combustion Engine Research Institute, Tianjin University，Tianjin 300072,China
Cao Yuhe	School of Electrical and Information Engineering, Tianjin University，Tianjin 300072,China
Cui Te	School of Electrical and Information Engineering, Tianjin University，Tianjin 300072,China
Ren Chao	School of Electrical and Information Engineering, Tianjin University，Tianjin 300072,China

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中文摘要:

水下游动机械臂（underwater swimming manipulator, USM）是一种由水下蛇形机器人和矢量推进器组成的新型水下机器人。USM系统具有高度非线性、强耦合以及不确定性等特点，其动力学模型难以精确建立。因此，实现USM的高精度镇定控制存在挑战。针对这一问题，本文基于反馈线性化和自适应径向基函数神经网络（radial basis function neural network, RBFNN），设计了一种动力学控制方案以实现USM的镇定控制。首先，介绍了USM平台结构，基于Lagrange方程给出了USM的动力学模型，并推导了USM的矢量推力系统模型。然后，设计了基于反馈线性化和RBFNN的动力学控制器，并通过反步法自适应更新RBFNN的权重。其中，权重自适应更新RBFNN用于实时估计系统未建模部分、参数误差以及外部扰动，从而对动力学控制器进行补偿。此外，为了将动力学控制器提供的广义力和力矩转换成各个执行器的控制输入，给出了推力分配策略。最后，进行了湖泊实验，分别对USM的I构型和C构型镇定控制，文章所提出的控制方案在两种构型下的稳态误差均小于0.08 m和10°，验证了所提出的USM六自由度镇定控制器的有效性。

英文摘要:

The underwater swimming manipulator (USM) is a new type of underwater robot composed of an underwater snake robot and several thrusters. The USM system has the characteristics of high nonlinear and uncertainty, and its dynamic model is difficult to establish accurately. Therefore, it is challenging to achieve high precision stabilization control of USM. To solve this problem, this paper designs a dynamic control framework based on feedback linearization and adaptive radial basis function neural network (RBFNN) for USM stabilization control. Firstly, the structure of the USM platform is introduced, the dynamic model of the USM is established based on the Lagrange equation, and the model of the vector thrust system is derived. Then, a dynamic controller based on feedback linearization and RBFNN is designed, and the weight of RBFNN is updated adaptively by backstepping method. Among them, the weight adaptive updating RBFNN is used to estimate the unmodeled part of the system, parameter errors and external disturbances, so as to compensate the dynamics controller. In addition, in order to convert the generalized forces and torques provided by the dynamic controller into the control inputs of each actuator, a thrust distribution strategy is given. Finally, lake experiments are carried out to stabilize the I-shape and C-shape of USM respectively. Compared with traditional methods, the steady-state errors of the proposed control scheme under both configurations are less than 0.08 m and 10°, which verifies the effectiveness of the proposed 6-DOF USM stabilization controller.

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