An improved model predictive control approach for fuel efficiency optimization of vessel propulsion systems

• Published in: Control engineering practice 2021-04, Vol.109, p.104749, Article 104749
• Main Authors: Wang, Jia, Han, Huayun, Yang, Ying, Wei, Muheng
• Format: Article
• Language: English
• Subjects: Model predictive control; Nonlinear control systems; On-line policy improvement; Suboptimality estimate; System identification
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2021.104749

This paper studies the fuel efficiency improvement issues for the vessel propulsion systems (VPSs). Specifically, the fuel efficiency is optimized by a novel model predictive control (MPC) approach. Our study consists of two parts. In the first part, using only the measurable data, the predictive model is constructed with the aid of the sparse regression method (SRM). The second part is devoted to improving the fuel efficiency of the VPS based on the suboptimality estimate of the MPC scheme. A complete design method of the model predictive controller for the VPSs is given in this paper, which consists of the data-driven system identification, controller formulation and on-line closed-loop performance improvement. A case study on a very large ore carrier (VLOC) propulsion system is given in the end to show that the proposed approach achieves better fuel efficiency than the traditional MPC scheme. •The fuel efficiency improvement issues for the vessel propulsion systems are studied.•The dynamics model of the vessel propulsion system is constructed via a data-driven method, which can be implemented easily in the practical applications.•A novel MPC scheme is proposed to improve the fuel efficiency with the aid of the suboptimality estimate.•The experiment results based on the measurable data of a real vessel show that the proposed control scheme improves the fuel efficiency.