A SDNN-MPC method for power distribution of COGAG propulsion system

Jian Li, Zhitao Wang, Shuying Li and Liang Ming

Energy, 2022, vol. 254, issue PB

Abstract: In order to solve the problems of power distribution taking a long time and large fluctuation of propeller speed in the process of COGAG propulsion system switching operating patterns, model predictive control (MPC) is introduced to control power distribution and a SDNN-MPC method is proposed. Discrete incremental LPV model is established as the prediction model and MPC for power distribution process is designed. In order to make MPC suitable for COGAG, which is a nonlinear time-varying system with short sampling period, a simplified dual neural network (SDNN) with simplified constraints is used to solve the quadratic programming in MPC. A large number of simulation results show that the established LPV model has high prediction accuracy and SDNN is helpful to shorten the execution time of MPC. The softening factor and weight factors in MPC can affect the performance of power distribution control. Two principles are summarized to choose weight factors and the recommended range of the softening factor is 0.026–0.034. Compared with the PI controller, based on the SDNN-MPC the time of power distribution process is shortened and the fluctuation of propeller speed is reduced. The maneuverability and stability of the vessel are improved.

Keywords: COGAG propulsion System; Model predictive control; Simplified dual neural network; Power distribution (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012130

DOI: 10.1016/j.energy.2022.124310

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