Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

Abstract

This article addresses the control issues of underwater manipulator arms in complex marine environments, proposing a composite control strategy based on the Harris Hawk Optimization (HHO) algorithm and Radial Basis Function (RBF) neural network. Combining the global search capability of the HHO algorithm with the fast approximation characteristics of RBF neural networks, a self-adaptive control method for underwater manipulator arms is designed. By automatically optimizing the parameters of the neural network, the performance and robustness of the control system are enhanced. Through simulation experiments, the effectiveness of the proposed control algorithm is verified. The results show that compared with traditional RBF neural network control, the proposed optimization control algorithm significantly improves the traditional RBF neural network control, demonstrating good control effects and higher practical value, providing an effective solution for the precise control of underwater manipulator arms.