Fixed-time adaptive neural control for output-constrained trajectory tracking of unknown marine surface vehicle

2025/02/13 by Qijia Yao, Qing Li, Hadi Jahanshahi
Engineering · Computer Science · #Adaptive Control of Nonlinear Systems #Adaptive Dynamic Programming Control #Fault Detection and Control Systems

paper · doi:10.1080/00207179.2025.2461594

Abstract

This article develops a fixed-time adaptive neural control method for the trajectory tracking of unknown marine surface vehicle (MSV) with output constraints. The developed controller is recursively designed under the fixed-time backstepping control framework. The barrier Lyapunov function (BLF) is introduced to handle the output constraints. The neural network (NN) identification and parametric adaptation technique are separately adopted to compensate for the model unknowns and disturbances. The stability argument shows that all error variables under the developed controller can regulate to the small neighbourhoods about zero in fixed time. The developed controller has the following two distinctive features. (1) The developed controller can acquire the fixed-time trajectory tracking while guaranteeing the constrained outputs simultaneously. (2) The developed controller is not only insensitive to model unknowns but also robust against disturbances. Lastly, simulated studies are given to demonstrate the obtained results.

Related

• Adaptive Control for Marine Vessels Against Harsh Environmental Variation
• Robust Adaptive Neural Network Control of Time-Varying State Constrained Nonlinear Systems
• Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters
• A neural network based controller for underwater robotic vehicles
• Fixed-time trajectory tracking control for unmanned surface vessels in the presence of model uncertainties and external disturbances