Submarine Multi-Model Switching Control Under
Full Working Condition Based on Machine Learning

doi:10.1007/s12204-021-2284-6

摘要/Abstract

Abstract: A continuous submarine depth control strategy based on multi-model and machine learning switching method under full working condition is proposed in this paper. A submarine motion model with six-degree-of freedom is first built and decoupled according to the force analysis. The control set with corresponding precise model set is then optimized according to different working conditions. The multi-model switching strategy is studied using machine learning algorithm. The simulation experiments indicate that a multi-model controller comprised of the proportional-integral-derivative (PID), fuzzy PID (FPID) and model predictive controllers with support vector machine (SVM) switching strategy can realize the continuous submarine depth control under full working condition, showing a good control performance compared with a single PID controller.

Key words: multi-model switching control, machine learning, model decoupling, submarine

中图分类号:

TP 181

. [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(3): 402-410.

LIANG Liang1 (梁良), SHI Ying1∗ (石英), MOU Junmin2∗ (牟军敏). Submarine Multi-Model Switching Control Under Full Working Condition Based on Machine Learning[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(3): 402-410.

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14	Machine Learning-Based Approach to Liner Shipping Schedule Design
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Submarine Multi-Model Switching Control Under Full Working Condition Based on Machine Learning

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