Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm.

Area of Science:

• Robotics and Control Systems
• Ocean Engineering

Background:

• Autonomous Underwater Vehicles (AUVs) present complex nonlinear and coupled system dynamics.
• Effective motion control and path optimization are critical for AUV mission success.

Purpose of the Study:

• To investigate the application of fuzzy adaptive PID controllers and improved genetic algorithms (IGA) for AUV motion control and path optimization.
• To develop and validate a control model for AUV heading, climb angle, and depth.
• To compare the performance of the proposed methods through experimental simulations.

Main Methods:

• Establishment of a basic coordinate system for the AUV.
• Analysis of spatial forces acting on the AUV to derive the control model.
• Investigation and implementation of fuzzy adaptive PID control and IGA techniques.
• Conducting experimental simulations to evaluate control and optimization performance.

Main Results:

• Fuzzy adaptive PID controllers and IGA demonstrate effectiveness in AUV motion control and path optimization.
• The fuzzy adaptive PID method offers advantages such as reduced overload and enhanced system stability.
• The proposed methods are suitable for the demanding requirements of AUV navigation.

Conclusions:

• The integration of fuzzy adaptive PID control and IGA provides a robust solution for AUV motion control and path optimization.
• The developed control strategies contribute to improved AUV performance and mission capabilities.
• This research validates the practical applicability of advanced control algorithms in underwater robotics.