Tuning Rules for Active Disturbance Rejection Controllers via Multiobjective Optimization—A Guide for Parameters Computation Based on Robustness

Abstract

A set of tuning rules for Linear Active Disturbance Rejection Controller (LADRC) with three different levels of compromise between disturbance rejection and robustness is presented. The tuning rules are the result of a Multiobjective Optimization Design (MOOD) procedure followed by curve fitting and are intended as a tool for designers who seek to implement LADRC by considering the load disturbance response of processes whose behavior is approximated by a general first-order system with delay. The validation of the proposed tuning rules is done through illustrative examples and the control of a nonlinear thermal process. Compared to classical PID (Proportional-Integral-Derivative) and other LADRC tuning methods, the derived functions offer an improvement in either disturbance rejection, robustness or both design objectives.

Optimal Active Disturbance Rejection Control for Second Order Systems

“…By solving the optimization problem in (26) for different values of ω c and ζ for a second-order underdamped system, the results are shown in Fig. 2.…”

“…The results in Fig. 4 were obtained by solving the optimization problem in (26) for different values of ω c and a for the overdamped second-order system in (28). In general, higher values of the parameter a require higher values of ω c for the same fixed value of λ.…”

“…Moreover, in [25], a method was presented that started with generalized active disturbance rejection control (GADRC) with known plant information and obtained parameters for a reduced-order ADRC (RADRC) through approximation with GADRC. Other solutions to the ADRC parameter tuning problem are discussed in [26]- [28].…”

Active disturbance rejection control: an application to continuous microalgae photobioreactors

“…Parameter tuning There are several methods available in the literature to tune ADRC, such as: margins approaches, step response curves, multiobjective optimization problems, and minimizing performance indices. [38][39][40] Another often-used approach to tune ADRC parameters is based on bandwidth. 41 This path expresses ⊗ obs and K as a function of two quantities called observer bandwidth (ω 0 ) and controller bandwidth (ω c ).…”

“…There are several methods available in the literature to tune ADRC, such as: margins approaches, step response curves, multi‐objective optimization problems, and minimizing performance indices 38‐40 . Another often‐used approach to tune ADRC parameters is based on bandwidth 41 .…”