Enhancing Trajectory Tracking Performance of Underwater Gliders Using Finite-Time Sliding Mode Control Within a Reinforcement Learning Framework

Underwater gliders, as autonomous underwater vehicles, are integral to oceanographic research, environmental monitoring, and military applications. Given the intricate and ever-changing underwater environment, the precise management of an underwater glider’s dive depth and pitch angle is imperative for optimal functionality.This study introduces a finite-time sliding mode control method for controlling dive depth and pitch angle of underwater gliders. It incorporates a radial basis function neural network in a critic–actor reinforcement learning framework, enhancing navigational performance in difficult conditions. Sea trial data are used to create a dynamic model for the underwater glider, which is then used to design a control law. Sliding mode control is used to align the dive depth and pitch angle with the desired trajectory. Actor and critic neural networks are used to handle disturbances and evaluate error costs. By incorporating standard deviation update technique into actor and critic neural networks, along with weight updates, we improve controller stability and reduce errors in maintaining dive depth and pitch angle. Our approach is proven to be more effective than traditional SMC and reinforcement learning SMC methods in trajectory tracking, even in the presence of disturbances, as shown in the simulation results.