Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances
DOI10.1002/oca.3050OpenAlexW4386625884MaRDI QIDQ6139608
Publication date: 19 January 2024
Published in: Optimal Control Applications and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/oca.3050
neural networktrajectory trackingmodel predictive controlautonomous underwater vehicleschaotic sparrow search algorithm
Artificial neural networks and deep learning (68T07) Approximation methods and heuristics in mathematical programming (90C59) Automated systems (robots, etc.) in control theory (93C85) Model predictive control (93B45)
Cites Work
- Set invariance in control
- Distributed implementation of nonlinear model predictive control for AUV trajectory tracking
- Trajectory tracking control based on a virtual closed-loop system for autonomous underwater vehicles
- An adaptive chaos particle swarm optimization for tuning parameters of <scp>PID</scp> controller
- Robust guaranteed cost control for time‐delay fractional‐order neural networks systems
- Neural network-based nonlinear sliding-mode control for an AUV without velocity measurements
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