There are strong coupling relationships among temperature, pressure, flow rate and other parameters in the process of human specimen fluid exchange, which are easily affected by external disturbances, leading to the decrease of control accuracy and response lag. In this paper, a real-time adaptive regulation method based on fuzzy control theory for human specimen liquid exchange process is proposed to solve the problems of poor stability and weak anti-interference ability of traditional PID control when facing a nonlinear, multivariable coupled system. The study designed a two-input and three-output fuzzy adaptive PID controller to adjust the PID parameters in real time with the error and its derivatives as the control inputs, at the same time, the hybrid adaptive particle swarm algorithm (HAPSO) was proposed to optimize the parameters of the fuzzy controller through the introduction of the relative evolution factor and the particle diversity factor, and combined with the optimization strategy of the small living environment. Simulation results show that the HAPSO-optimized fuzzy adaptive PID control system reduces the overshooting amount from 10% to 3.5%, the stabilization time from 21 s to 17 s, and the liquid level fluctuation range from 18 cm to 6 cm compared with the traditional PID control system. In addition, the optimized system can be quickly stabilized at 16 °C and keep smaller fluctuation when the external environment changes in the temperature control test, which showing excellent robustness. The study proves that the adaptive regulation method based on fuzzy control theory and HAPSO optimization can effectively improve the control accuracy and stability of the human specimen liquid exchange process, which provides a new technical solution for the automation control in related fields.
