Predictor-based practical fixed-time adaptive sliding mode formation control of a time-varying delayed uncertain...

Area of Science:

• Robotics and Control Systems
• Marine Engineering
• Nonlinear Control Theory

Background:

• Surface vessel control is challenged by unknown dynamics, disturbances, input saturation, and time-varying delays.
• Achieving fixed-time formation control (FTFC) requires robust strategies to handle these complex uncertainties.

Purpose of the Study:

• To develop a novel fixed-time formation control (FTFC) strategy for fully-actuated surface vessels (FASVs).
• To effectively address complex uncertainties including unknown dynamics, disturbances, input saturation, and time-varying delays.

Main Methods:

• A state predictor (SP) strategy combined with state transformation (ST) to handle time delays and ensure fixed-time stability.
• Integration of a predictor-based neural network to identify complex system unknowns.
• Incorporation of an adaptive terminal sliding mode (ATSM) controller using a time base generator (TBG) for reduced control inputs and smooth convergence.

Main Results:

• The proposed FTFC scheme successfully predicts system states, mitigating time-delay issues.
• Neural networks effectively identify and compensate for unknown dynamics and disturbances.
• The ATSM controller ensures smooth convergence and reduced control effort, demonstrating superior performance through simulations and comparisons.

Conclusions:

• The developed FTFC strategy provides a robust and effective solution for controlling FASVs under complex uncertainties.
• The combination of SP, ST, neural networks, and ATSM offers enhanced performance and stability.
• Simulation results validate the superiority and effectiveness of the proposed control scheme.