Model-free recursion-based control for 5-degree-of-freedom under-actuated Autonomous Underwater Vehicles via online-tranining neural network

Tóm tắt

The ability to operate in underwater conditions autonomously without human intervention is one of the most intrigued salient features of Autonomous Underwater Vehicles (AUVs), directly leading to profound attention from the scientific community in recent years. However, the scientific research focusing on improving the AUVs’ operation is challenged due to the lack of actuators, unknown dynamics, uncertain parameters and strong nonlinearities. To overcome the technical restriction caused by the actuator shortfall, this paper introduces a new control strategy for under-actuated AUVs (UAUVs) with five degrees of freedom, utilising the recursion technique and an artificial neural network. The recursion technique in this paper is designed based on a new modified formula for tracking errors, with a double-loop configuration, resulting in the equivalence to a strictly feedback nonlinear system of under-actuated AUVs. Meanwhile, the neural network used in this paper not only addresses the system’s uncertainties but also enhances the controller’s adaption. Furthermore, the network’s learning rule is implemented online, thereby reducing the computational burden and maintaining the AUVs’ stability over the course of the training process. The effectiveness of the controller is verified by sophisticated numerical simulation on Matlab and Simulink platforms. Compared to other existing methods, such as traditional Backstepping control, the proposed method offers a smaller tracking error approximately 20%. The proposed method contributes to the class of control strategies for under-actuated AUVs in a specific speaking and for under-actuated uncertain nonlinear systems in general speaking.