The control component of a generator excitation system is key to its smooth operation and ability to effectively resist unexpected situations. In this study, we design a control strategy for the excitation system of a high-temperature superconducting motor that combines fuzzy control with conventional PID control. The method is based on a conventional PID with a fuzzy inference algorithm and variable universe fuzzy control added. Fuzzy control is realized by the computer processor through the selection of the scalable factor function model and fuzzy control rules, further improving the precision of the control system. To facilitate its transplantation into other control strategies, we adopt the discrete time system for the control strategy design. We establish an excitation system simulation, through which we compare fuzzy PID control and variable universe fuzzy PID control, and find that variable universe fuzzy PID control has better dynamic and static performance.
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