Optimization of Active Disturbance Rejection Controller for Distillation Process Based on Quantitative Feedback Theory

The continuously increasing requirements for product purity and heat exchange efficiency in distillation processes exacerbate the system’s nonlinearity, coupling effects, and uncertainties. To address these challenges, this research proposes an optimized design approach for multivariable active disturbance rejection control (ADRC) that integrates quantitative feedback theory (QFT). An extended state observer is first employed to estimate and compensate for coupling and uncertainties, thus enabling effective decoupling. Under a two-degree-of-freedom equivalent model, QFT performance boundaries are transformed into a fitness function, turning controller parameter tuning into a frequency-domain multi-objective optimization problem. An improved multi-objective grey wolf algorithm is then introduced to optimize the controller parameters. The proposed approach is verified in a toluene–methylcyclohexane (MCH) extractive distillation process and compared with proportional–integral (PI) control and model predictive control (MPC). The simulation results indicate that, under the same feed temperature disturbance, the ADRC–QFT strategy reduces the system settling time by over 67% and lowers the integral of absolute error (IAE) index by more than 53% compared with PI–QFT and MPC, while also exhibiting stronger robustness to model uncertainties. These findings suggest that the proposed method provides an effective solution for achieving high precision and robust control in complex coupled distillation processes.