Focus on the uncertainty of Medical closed-loop control System caused by differences among patients, a model-based closed-loop adaptive control architecture was proposed based on the concept of MCPS. BIS signal was used as the control variable to control the depth of hypnosis during anesthesia. The pharmacokinetic and pharmacodynamic model of the patient was used in the control scheme, and the calculated results were used as the feedback signal of the standard PID controller to correct the uncertainty of the model. In the model, parameter adjustment is performed offline using genetic algorithm to optimize the performance indicators of the patient data set. The infusion rate of anesthetic drugs can be automatically adjusted to keep the depth of anesthesia at a stable target value. The robustness of the proposed method was tested by adding noise blocks. Monte Carlo method was used to verify the effectiveness of the proposed method on a wide range of people. Simulation results show that the proposed method can reach the target value stably in a specified time and has good interference rejection.