Temperature control system modeling method based on adaptive GWO for quantum sensor

The atomic vapor cell, as the core sensing element in quantum sensors, requires precise temperature control modeling to ensure thermal stability and sensing accuracy. However, modeling is challenged by complex material properties and uncertain thermal parameters. This paper proposes a modeling approach based on an adaptive grey wolf optimizer (GWO). The research comprises three key aspects: (1) Establishing a second-order plus dead time (SOPDT) model through thermal conduction mechanism analysis. (2) Constructing a training dataset by integrating time-domain and frequency-domain response characteristics. (3) Developing an enhanced adaptive GWO algorithm for precise system parameter identification. Experimental results demonstrate that the goodness of fit between the proposed model and experimental test dataset reached 99.6%, confirming the effectiveness of the proposed modeling approach. This modeling method is of great significance for controller design of temperature control system and enhancing the closed loop system’s performance.