Trajectory Following Control of an Unmanned Vehicle for Marine Environment Sensing

Area of Science:

• Robotics
• Control Systems Engineering
• Ocean Engineering

Background:

• Autonomous surface vehicles (ASVs) are crucial for marine environmental sensing.
• Navigating ASVs in dynamic marine conditions (currents, waves, wind) requires robust trajectory control.
• Existing controllers struggle with marine environment complexities and trajectory errors.

Purpose of the Study:

• To propose a robust discrete-time super-twisting second-order sliding mode controller (DSTA) for ASV trajectory tracking.
• To enhance controller robustness and minimize chattering effects.
• To optimize controller gains using a genetic algorithm (GA).

Main Methods:

• Discretization of the ASV model using integral approximation, treating disturbances as perturbations.
• Estimation of perturbations using a time delay estimator (TDE) for improved robustness.
• Optimization of controller gains via a genetic algorithm (GA) minimizing tracking error and control energy.
• Stability analysis using a Lyapunov approach.

Main Results:

• The proposed GA-tuned DSTA controller demonstrated superior trajectory tracking accuracy compared to DSMR and heuristically tuned DSTA.
• The controller effectively suppressed chattering effects.
• Robust performance was confirmed in simulations with and without external disturbances.

Conclusions:

• The GA-DSTA offers a robust and effective solution for ASV trajectory control in challenging marine environments.
• The TDE significantly enhances robustness, allowing for smaller controller gains.
• The proposed controller outperforms existing methods in tracking accuracy and chattering attenuation.