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

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.