Enhanced tuning of Smith predictor based series cascaded control structure for integrating processes

Area of Science:

• Control Engineering
• Industrial Automation
• Process Control

Background:

• Industrial integrating plants often present control challenges due to their inherent dynamics.
• Existing control strategies may require extensive tuning and lack robustness against process variations.

Purpose of the Study:

• To develop an enhanced series cascaded control approach incorporating a Smith predictor.
• To address the control of industrial integrating plants with improved performance and systematic tuning.

Main Methods:

• The proposed method combines cascaded control, Smith predictor, moment matching, and outer-loop decomposition.
• For second-order outer-loop models, outer-loop decomposition splits them into first-order models.
• Controller parameters are determined using moment matching by equating Maclaurin series expansions.

Main Results:

• The enhanced control scheme demonstrates significant improvements in closed-loop response compared to recent strategies.
• The method provides a systematic procedure for tuning controller parameters to achieve desired sensitivity.
• The approach shows robustness even when process dynamics are perturbed.

Conclusions:

• The developed enhanced series cascaded control with Smith predictor offers a superior alternative for controlling industrial integrating plants.
• Moment matching provides a more direct and less empirical method for controller tuning.
• The proposed strategy enhances control performance and robustness, outperforming existing methods.