Disturbance observer-based control with partially coupled inverse model for stable TITO processes with time delays

Area of Science:

• Control Engineering
• Process Systems Engineering
• Industrial Automation

Background:

• Multivariable processes with multiple time delays present significant control challenges.
• Existing multivariable disturbance observer-based (MDOB) control methods often use diagonal or full inverse models, limiting disturbance rejection.
• Effective control strategies are crucial for stable operation and performance in complex industrial systems.

Purpose of the Study:

• To develop a modified MDOB control method for stable two-input-two-output (TITO) processes with multiple time delays.
• To enhance disturbance rejection performance compared to existing MDOB strategies.
• To provide a systematic approach for designing and tuning the proposed control method.

Main Methods:

• A partially coupled inverse model is proposed, differing from diagonal or full inverse models.
• A novel interaction measure, relative input disturbance gain (RIDG), is defined to evaluate disturbance rejection.
• A single iteration strategy is employed to derive filter elements, and analytical forms are obtained by minimizing the 2-norm of the outputs.
• Filter parameters are tuned based on performance and robustness criteria.

Main Results:

• The proposed partially coupled inverse model demonstrates superior disturbance rejection capabilities.
• The single iteration strategy effectively handles cross terms in filter elements.
• Analytical solutions for filter elements are derived, facilitating practical implementation.
• Simulation examples validate the effectiveness and superiority of the proposed MDOB control strategy.

Conclusions:

• The modified MDOB control method offers improved disturbance rejection for TITO processes with time delays.
• The use of a partially coupled inverse model and RIDG provides a more effective control design.
• The presented method offers a robust and high-performance solution for complex industrial control applications.