Delay robustness-constrained control of integrating processes with optimal tracking and quantified disturbance rejection.

Regarding a general class of integrating processes subject to uncertain delays, this paper investigates a two-degree-of-freedom (2-Dof) control scheme with a proportional-derivative (PD) controller and disturbance observer (DOB). Relative delay margin is introduced as a paramount metric to evaluate the delay robustness, with which a set of novel and explicit tuning formulae for PD controller is analytically derived under the single external loop. In this individual frame, the stability boundaries associated with the governing parameters are first studied, indicating the nominal stability conditions for the 2-Dof control system. Then the optimal tracking problem is formulated and addressed with such delay robustness constraints. For the design of the internal loop, the performance of DOB will be quantified by the trade-off between the external relative delay margin and the disturbance rejection, thus retaining the original PD controller design. Besides, the stability in the nominal delay range is primarily concerned when demarcating the low-pass filter in DOB, based on which a critical time constant is obtained through exhaustive testing. Following the route of combining analytical design with quantitative adjustment, the synthetic tuning rules can provide prescribed robustness against delay uncertainty for integrating processes. Through conducting illustrative simulations and a water tank experiment, the efficiency and merit of the proposed scheme are demonstrated.

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.