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Robust Fixed-Time H∞ Trajectory Tracking Control for Marine Surface Vessels Based on a Self-Structuring Neural
Xuehong Tian1,2, Zhicheng Wang1, Jianbin Yuan1
1School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
Computational Intelligence and Neuroscience
|July 18, 2022
Summary
This study introduces a robust fixed-time H∞ trajectory tracking controller for marine surface vessels using a self-structuring neural network. The controller ensures stability and enhances performance despite uncertainties and disturbances.
Area of Science:
- Marine Engineering
- Control Systems
- Artificial Intelligence
Background:
- Marine surface vessels (MSVs) require precise trajectory tracking for safe and efficient operation.
- Existing control methods often struggle with model uncertainties, environmental disturbances, and actuator faults.
- Robust control strategies are essential to maintain performance under dynamic and unpredictable conditions.
Purpose of the Study:
- To develop a robust fixed-time H∞ trajectory tracking controller for MSVs.
- To enhance the resilience of MSV control systems against model uncertainties, environmental disturbances, and actuator faults.
- To ensure fixed-time stability and bounded performance within a predetermined time.
Main Methods:
- Proposal of a fixed-time H∞ Lyapunov stability theorem for guaranteeing fixed-time stability (FTS) and bounded L2 gain.
- Design of a self-structuring neural network (SSNN) to compensate for lumped disturbances, including model uncertainties, environmental factors, and actuator faults (AFs).
- Real-time adjustment of the SSNN structure using elimination and split rules to optimize computational load and control efficacy.
Main Results:
- The proposed fixed-time H∞ Lyapunov stability theorem guarantees FTS and bounded L2 gain for the MSV closed-loop system.
- The SSNN effectively compensates for system uncertainties and disturbances, demonstrating high accuracy and robustness.
- Lyapunov stability proofs confirm that all signals within the MSV system remain stable and bounded within a specified time.
Conclusions:
- The developed control scheme offers a feasible and effective solution for robust trajectory tracking in MSVs.
- The SSNN-based approach provides significant advantages in handling complex disturbances and reducing computational burden.
- This research contributes to advancing the autonomy and reliability of marine surface vessels through advanced control techniques.
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