Control System of Autonomous Surface Vehicle

Dear Colleagues,

Autonomous Surface Vehicles are at the forefront of maritime technology, with applications spanning across industrial, commercial, and scientific domains. The control system is the nucleus of autonomous surface vehicle functionality, encompassing a varied disciplines, including nonlinear control design, fuzzy logic control, and observer design, to ensure reliable and efficient operation on the water.

This Special Issue will present a curated selection of articles that break new ground on the control systems of autonomous surface vehicles. Our focus is on presenting advancements in dynamic modeling, optimization, and control methodologies, paired with experimental studies that bridge the gap between theory and practice. This Special Issue covers varied contributions from different fields.

The control systems of autonomous surface vehicles follow a highly integrated approach that utilizes advanced control techniques to ensure that the vehicle performs efficiently, safely, and autonomously on water. The role of actuators in this context is crucial as they execute the commands generated by the control algorithms to drive the vehicle's motion, maintain stability, and perform tasks. The following points highlight how the specific topics align with the journal's focus:

1. Surface Vehicle Control: This refers to the overall management of an autonomous surface vehicle’s movement on the water surface, encompassing both steering and propulsion. Actuators like rudders (for steering) and propellers (for propulsion) are directly controlled based on the inputs from the control system to achieve desired motion and positioning.

2. Nonlinear Control Design: Many dynamics of autonomous surface vehicles, especially in turbulent water conditions, are nonlinear. Nonlinear control techniques help handle complex and time-varying dynamics. The actuators (propulsion and steering systems) need to respond in a nonlinear manner to inputs such as thrust adjustments or rudder angles, ensuring that the autonomous surface vehicle can maintain stable operation despite external disturbances (waves, winds, etc.).

3. Fuzzy Logic Control: Fuzzy logic control systems allow for handling uncertainty and imprecision in the control commands. This can be used to fine-tune actuator commands for navigation, collision avoidance, or maneuvering in complex environments. Fuzzy controllers typically adjust the actuator outputs (e.g., speed and direction of the propellers and rudder) based on fuzzy rules derived from environmental conditions and system states.

4. Observer Design: Observers are used to estimate unmeasured states (such as velocity, position, or unknown disturbances) in an autonomous surface vehicle. These estimates feed into the actuator control algorithms. For example, an observer might estimate the real-time motion of the vehicle in the presence of unmeasured forces (such as wind or waves), allowing the actuators to adjust accordingly and maintain the desired path.

5. Automatic Collision Avoidance: In collision avoidance, actuators play a critical role in executing the planned evasive maneuvers. The control system computes the necessary changes in the speed and heading of the autonomous surface vehicle (using the actuators like rudders and propellers) to avoid obstacles, relying on sensors and the control system's algorithms to drive the actuators in real time.

6. Sliding Mode Control Systems: Sliding mode control is a robust method for handling uncertainties and ensuring stability, particularly in the presence of external disturbances (such as waves or other dynamic forces). Sliding mode controllers ensure that the actuators (rudder and propulsion units) adjust to maintain the desired trajectory, even under uncertain or rapidly changing conditions.

7. Trajectory Planning and Optimization: Trajectory planning optimizes the path of the autonomous surface vehicle to reach a destination while considering constraints such as speed, obstacle locations, and fuel consumption. Actuators are used to follow the planned trajectory by continuously adjusting the steering angle and propulsion power based on the control system's output.

8. Autonomous Surface Vehicle Control: The control system coordinates all aspects of the autonomous surface vehicle’s operation, ranging from navigation to interaction with the environment. The actuators are essential for converting the system's decision-making (generated by the control algorithms) into physical actions that allow the autonomous surface vehicle to move, steer, and adapt to changing conditions autonomously.

9. Networked Control: In cases where multiple autonomous surface vehicles are involved, networked control allows for coordinated motion between vehicles. The actuators of each autonomous surface vehicle are controlled not only by local sensors and algorithms but also by communication with other autonomous surface vehicles or a central controller. This coordination may involve synchronized maneuvers and collective trajectory adjustments, with each autonomous surface vehicle's actuators being controlled based on shared information.

Dr. Jun Ning
Dr. Xin Hu
Prof. Dr. Zhouhua Peng
Prof. Dr. Jian-Chuan Yin
Guest Editors

Manuscript Submission Information

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• surface vehicle control
• nonlinear control design
• fuzzy logic control
• observer design
• automatic collision avoidance
• sliding mode control systems
• trajectory planning and optimization
• autonomous surface vehicle control
• networked control