Robust controller design for marine electric propulsion system over controller area network. (August 2020) - Peng, Huachao; Zhu, Xiaoyuan; Yang, Liu; Zhang, Guichen
Abstract: With the consistent tendency of electrification and intelligentization of marine systems in recent years, the electric propulsion system has been a prospective candidate for ships due to its potential in energy saving, environmental protection, high efficiency, and high reliability. However, time-varying delays of communication networks would be induced due to increasing information exchange requirement that caused by the increasing number of devices connecting to the communication networks as well as bandwidth limitation of marine network. Meanwhile, the propeller load is difficult to model due to the complexity of ship dynamics, which increases the uncertainty of marine electric propulsion system. Thus, robust control of the marine electric propulsion system is necessary and challenging. This paper presents a mixed H ∞ ∕ L Q R robust controller design for marine electric propulsion system to address robust speed tracking problem under possible network-induced delays. The propeller dynamic model is built via open-water experimental data to reflect propeller load characteristics. Then, a delay-free model is established for the marine electric propulsion system by using system augmentation technique, with the random network-induced delay characterized by polytopic inclusions and Taylor series expansion. A mixed H ∞ ∕ L Q R robust controller design is proposed for the marine electric propulsion system. Finally, based on a detailed controller area network model, the performance and effectiveness of the mixed H ∞ ∕ L Q R robust controller are demonstrated by comparative simulation tests of a marine electric propulsion system with 4.088 MW permanent magnet synchronous motor (PMSM). Highlights: A discrete-time model is developed for marine electric propulsion system. A robust controller is proposed against the disturbances. Comparative simulation tests using detailed CAN model are conducted.