Analytical tuning rules for second-order reduced ADRC with SOPDT models.

Area of Science:

• Control Engineering
• Systems Science
• Automation

Background:

• Second-Order Plus-Dead-Time (SOPDT) models are common in industrial processes.
• Tuning controllers for SOPDT systems presents challenges in balancing performance objectives.
• Existing control methods may not adequately address both tracking and disturbance rejection simultaneously.

Purpose of the Study:

• To develop novel analytical tuning rules for second-order reduced linear active disturbance rejection control (ADRC).
• To optimize the trade-off between tracking performance and disturbance rejection capabilities for SOPDT systems.
• To ensure closed-loop stability through a robust tuning framework.

Main Methods:

• Formulation of a multi-objective optimization problem with Integral Absolute Error (IAE) objectives and constraints on robustness and stability.
• Utilizing a Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) algorithm to find Pareto optimal solutions.
• Employing Grey Relational Analysis (GRA) to select a compromise solution and regression to derive analytical tuning rules.
• Validation using simulations and experimental data from benchmark systems, load frequency control, temperature, and DC motor systems.

Main Results:

• Development of separate tuning rules tailored for lag-dominated and delay-dominated SOPDT models.
• Demonstration of effective performance through simulations on benchmark systems and real-world applications.
• Experimental validation confirming the practical applicability and effectiveness of the proposed tuning rules.
• Inclusion of a stability condition derived from the dual-locus method within the tuning framework.

Conclusions:

• The proposed analytical tuning rules offer a systematic approach to designing ADRC for SOPDT systems.
• The method effectively balances tracking and disturbance rejection, leading to enhanced control performance.
• The validated rules provide a practical tool for engineers in various industrial control applications.