A learning feed-forward current controller for linear reciprocating vapor compressors

Zhengyu Lin, Jiabin Wang, David Howe

    Research output: Contribution to journalArticlepeer-review


    Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via a crank shaft. However, to ensure efficient and reliable operation under all conditions, it is essential that motor current of a linear compressor follows a sinusoidal current command with a frequency which matches the system resonant frequency. The design of a high-performance current controller for linear compressor drive presents a challenge since the system is highly nonlinear, and an effective solution must be low cost. In this paper, a learning feed-forward current controller for the linear compressors is proposed. It comprises a conventional feedback proportional-integral controller and a feed-forward B-spline neural network (BSNN). The feed-forward BSNN is trained online and in real time in order to minimize the current tracking error. Extensive simulation and experiment results with a prototype linear compressor show that the proposed current controller exhibits high steady state and transient performance.
    Original languageEnglish
    Pages (from-to)3383-3390
    Number of pages8
    JournalIEEE Transactions on Industrial Electronics
    Issue number8
    Early online date28 Oct 2010
    Publication statusPublished - Aug 2011


    • compressors
    • current control
    • learning control systems
    • linear motors
    • neural networks


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