Data-driven iterative tuning based active disturbance rejection control for FOPTD model

Active Disturbance Rejection Control (ADRC) emerges as a promising control method that can effectively handle uncertainties and disturbances. However, many model-based ADRC tuning methods turn laborious to achieve satisfactory control performance, when the critical process parameters are difficult to accurately obtain, especially the time delay information. To this end, this paper aims to propose a data-driven iterative tuning method for time-delayed ADRC (TD-ADRC). Based on parameter scaling technique, the quantitative correlation among control performance, robustness and normalized controller parameters are investigated. It is then used to design robust nominal controller. Then, based on the TD-ADRC inner-loop equivalent structure, an iterative feedback tuning (IFT) method is proposed to optimally obtain the nominal first order plus time delay (FOPTD) process model. Its unbiasedness and convergence are also described. With the empirical relations and the IFT stochastic approximation algorithm, a data-driven iterative tuning method for TA-ADRC is proposed, which allows a reasonable trade-off between system performance and robustness. Simulation results validate the efficacy of the proposed method, and a water-tank control experiment depicts a promising prospect in control practice.