ROV Motion Control Algorithm Based on RBF Neural Network Compensation

Abstract

In view of the motion control problem of the operation-type remotely operated vehicles(ROVs) under the uncertainty of model parameters and the disturbance of the external environment, an adaptive double-loop sliding mode control strategy based on radial basis function(RBF) neural network was proposed. Firstly, the integral sliding mode control method with an improved reaching law was adopted for controlling the position of the ROV’s outer loop, and the integral sliding mode control method with an exponential reaching law was adopted for controlling the speed of the ROV’s inner loop. Secondly, in order to further improve the chattering problem of sliding mode control, the hyperbolic tangent function was introduced as the sliding mode switching term. Subsequently, the RBF neural network control technology was used to estimate and compensate for the uncertain parameters and external disturbances of the ROV model. Finally, the stability of the whole closed-loop system was proved by using the Lyapunov stability theory, and the motion control of the operation-type ROV was simulated numerically. The simulation results verify that the controller designed in this paper can achieve precise control of ROV navigation and effectively suppress the influence of uncertain parameters of the model and external disturbances on ROV motion.

Keywords

• remotely operated vehicle
• motion control
• radial basis function
• adaptive double-loop sliding mode control
• neural network