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

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-offreedom 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.