A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation
Danjie Zhu, Lei Wang, Hua Zhang, Simon X. Yang

TL;DR
This paper introduces a GOA-based fault-tolerant control method for underwater vehicles that effectively manages thruster faults and actuator saturation, ensuring accurate trajectory tracking under various fault conditions.
Contribution
It presents a novel fault-tolerant control strategy combining backstepping, sliding mode control, and Grasshopper Optimization Algorithm for underwater vehicles with multi-thruster systems.
Findings
Effective fault compensation in simulations with thruster damage.
Robust trajectory tracking under environmental perturbations.
Faster convergence compared to traditional FTC methods.
Abstract
This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
