Research on Trajectory Tracking Control Method for Wheeled Robots Based on Seabed Soft Slopes on GPSO-MPC

Area of Science:

• Robotics
• Ocean Engineering
• Control Systems

Background:

• Wheeled underwater robots face trajectory control challenges on soft seabed slopes due to slippage and varying forces.
• Existing model predictive control (MPC) methods require manual tuning and struggle with dynamic environmental changes.

Purpose of the Study:

• To develop an adaptive control strategy for wheeled underwater robots operating in complex seabed terrains.
• To enhance the accuracy, robustness, and adaptability of trajectory tracking control systems.

Main Methods:

• A hybrid grey wolf-particle swarm optimization (GPSO) algorithm was developed to adaptively tune MPC parameters online.
• A kinematic model for a four-wheeled differential-drive robot was created, and an MPC controller with error-state linearization was implemented.
• GPSO incorporated hierarchical leadership and chaotic disturbance for improved global search and local convergence.

Main Results:

• The proposed GPSO-MPC method demonstrated superior performance compared to standard MPC and particle swarm optimization-MPC (PSO-MPC).
• Significant improvements were observed in tracking accuracy, heading stability, and control smoothness during simulations.
• The adaptive nature of GPSO-MPC proved effective in dynamic underwater environments.

Conclusions:

• The hybrid GPSO algorithm offers an effective solution for adaptive online tuning of MPC parameters for underwater robots.
• GPSO-MPC enhances control system performance and robustness in challenging soft slope seabed conditions.
• This approach supports more reliable seabed surveying, engineering, and environmental monitoring by intelligent ocean technologies.