The invention provides an underwater mechanical arm trajectory tracking control method based on an RBF neural network and a sliding mode surface. The underwater mechanical arm trajectory tracking control method comprises the following steps that an underwater dynamic model and an electric drive mathematical model are established; constructing a cascade dynamic equation set; setting an expected input signal and defining a related error; designing a sliding mode function of a fast terminal sliding mode surface, defining a Lyapunov equation expression and deriving the Lyapunov equation expression; designing a singular point eliminating function according to the singular point problem existing in the sliding mode function, and updating a derivation function of the Lyapunov equation; and designing an RBF neural network and a weight to carry out neural network approximation on a nonlinear term of a derivation function of the Lyapunov equation, and designing an electric drive auxiliary controller and a dynamics auxiliary controller. On the basis of considering the auxiliary control of the electric driving force, the rapid terminal sliding mode control method is improved, the stability and the convergence speed of the trajectory tracking control method of the underwater mechanical arm are improved, and specific requirements of different application occasions can be met.
本发明提出一种基于RBF神经网络与滑模面的水下机械臂轨迹跟踪控制方法,包括以下步骤:建立水下动力学模型及电驱动数学模型;构建级联动力学方程组;设定期望输入信号并定义相关误差;进行快速终端滑模面的滑模函数设计,定义李雅普诺夫方程表达式并对其求导;根据滑模函数存在奇异点问题设计消除奇异点的函数,并更新李雅普诺夫方程的求导函数;设计RBF神经网络及权重对李雅普诺夫方程求导函数的非线性项进行神经网络逼近,设计电驱动辅助控制器和动力学辅助控制器。本发明在考虑电驱动力的辅助控制的基础上,对快速终端滑模控制方法进行改进,不仅提高了水下机械臂轨迹跟踪控制方法的稳定性和收敛速度,而且能够适应不同应用场合的具体需求。
Underwater mechanical arm trajectory tracking control method based on RBF neural network and sliding mode surface
一种基于RBF神经网络与滑模面的水下机械臂轨迹跟踪控制方法
2025-03-04
Patent
Electronic Resource
Chinese
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