Pressure optimization control of low-pressure vacuum systems based on anti-disturbance Smith predictor compensation

In addressing the challenges posed by significant time delays and multiple disturbances in low-pressure vacuum systems, this paper proposes an anti-disturbance Smith predictor compensation control method. The method, based on the dynamic characteristics of the low-pressure vacuum system, integrates the synergistic advantages of feedforward and feedback control to design a composite controller with parameter self-adaptive tuning (AFFPI), which optimizes control parameters according to changes in the process state. Secondly, to address the challenge posed by the inherent lag characteristics of the vacuum system on control performance, a Smith predictor (SP) structure based on dynamic compensation was established in the internal loop, effectively solving the issue of slow response caused by significant time delays in the low-pressure vacuum system. Finally, an anti-disturbance filtering unit was introduced into the pressure feedback channel. By configuring appropriate filter gain factors, the unit alleviates fluctuations in the control signal caused by disturbances to the SP, preventing the performance degradation typically observed in traditional SP control methods under disturbances, thus achieving efficient and high-precision pressure regulation in the low-pressure vacuum system. The simulation results demonstrate that the proposed control method reduces the average settling time by 31.73 s, reduces the overshoot by 10.59%, and lowers the ITAE index by 1619.41 when compared to AFFPI, Mac-PID, and PID-Smith. Under four types of simulated disturbances, the method demonstrates the most optimal control performance, exhibiting an average enhancement of 19.66% in disturbance control performance in comparison to the Smith predictive control method that does not consider disturbances (AFFPI-Smith). The experimental findings demonstrate that the proposed method reduces the average settling time by 7.32 s and decreases the CV, TV, ITE, and ITAE indices by 1.8247 × 10 −5 , 0.385, 3.85, and 26.23, respectively. This results in improvements in control accuracy, control time, and stability. Specifically, compared with AFFPI-Smith, the ITAE is reduced by 4.72, and the disturbance performance is improved by 11.12%. The proposed method maintains excellent control performance even when process conditions or control target pressures are adjusted, and it demonstrates strong robustness and disturbance rejection capability.