Analytical tuning rules for Reduced-order Active Disturbance Rejection Control with FOPDT models through...

Area of Science:

• Control Engineering
• Process Control
• Automation Systems

Background:

• Active Disturbance Rejection Control (ADRC) is effective but complex to tune.
• Reduced-order ADRC (RADRC) simplifies implementation and tuning.
• First-order plus dead-time (FOPDT) models are common in industrial processes.

Purpose of the Study:

• Develop analytical tuning rules for RADRC applied to FOPDT models.
• Address conflicting control objectives: tracking, disturbance rejection, and robustness.
• Provide practical tuning guidelines for industrial applications.

Main Methods:

• Formulated tuning as a multi-objective optimization problem.
• Employed a Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) Algorithm for Pareto-optimal solutions.
• Utilized the Best-Worst based PROMETHEE method for solution selection.
• Developed analytical rules via linear regression, with separate rules for lag- and dead-time-dominated systems.
• Validated stability using small-gain theorem and dual-locus methods.

Main Results:

• Derived novel analytical tuning rules for RADRC on FOPDT systems.
• Demonstrated effective balancing of tracking, disturbance rejection, and robustness.
• Simulation results show superior performance compared to recent methods.
• Experimental validation confirms practical applicability.

Conclusions:

• The proposed analytical tuning rules offer an effective and practical solution for RADRC implementation.
• The rules provide a systematic approach to tune RADRC for FOPDT processes, enhancing control performance.
• Stability of the closed-loop system is theoretically confirmed.