RBF neural network sliding mode control on pitching motion of an underwater spherical vechicle

Abstract

The pendulum spherical underwater vehicle can realize the ascending and descending motion in water by using the internal pitch pendulum and the central propeller. It is the frequent swing of the pitch pendulum that causes the fluctuation of the speed of the robot and reduces the stability of the system. A two-stage sliding method is proposed for the pitch driving characteristics of the robot and it can control the pitch angle and the swing angle of the pitch pendulum of the robot at the same time. The neural network is introduced to compensate the interference items adaptively. The stability of the designed controller is proved in theory. The simulation and experimental results show that the proposed control method can realize the rapid control of the pitch angle of the robot and the frequency of the pitch pendulum. The complex swing is restrained effectively, and the stability of the underwater motion of the robot is improved.