Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

The high-purity distillation column system is strongly nonlinear and coupled, which makes it difficult to control. Active disturbance rejection control (ADRC) has been widely used in distillation systems, but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays. This paper designs a proportion integral-type active disturbance rejection generalized predictive control (PI-ADRGPC) algorithm to control the distillation column system with large time delay. It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control (PI-GPC), thereby improving the performance of control systems with large time delays. Since the proposed controller has many parameters and is difficult to tune, this paper proposes to use the grey wolf optimization (GWO) to tune these parameters, whose structure can also be used by other intelligent optimization algorithms. The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method, multi-verse optimizer (MVO) tuned PI-ADRGPC and MVO tuned ADRC. The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance. (c) 2022 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.

Automated summary

This paper proposes a Proportional Integral-type Active Disturbance Rejection Generalized Predictive Control (PI-ADRGPC) algorithm for controlling a high-purity distillation column system with large time delay. By replacing the PD controller in Active Disturbance Rejection Control (ADRC) with a Proportional Integral-type Generalized Predictive Control (PI-GPC), the performance of control systems with large time delays is improved. The grey wolf optimization (GWO) is used to tune the parameters of the proposed controller. Simulation results demonstrate that the proposed strategy achieves good reference tracking and disturbance rejection performance.