Application of infinite model predictive control methodology to other advanced controllers
M Abu-Ayyad1, R Dubay, J M Hernandez
1Department of Mechanical Engineering, University of New Brunswick, Fredericton, NB, Canada, E3B 5A3. abuayyad@unb.ca
ISA Transactions
|September 10, 2008
Summary
This study demonstrates the generic applicability of Infinite Model Predictive Control (IMPC) to enhance various advanced control strategies. IMPC integration improves control performance in both simulated and real-time industrial applications.
Area of Science:
- Control Engineering
- Process Automation
- Nonlinear System Control
Background:
- Advanced control strategies are crucial for optimizing industrial processes.
- Existing predictive control algorithms face challenges with system nonlinearities.
- Infinite Model Predictive Control (IMPC) offers a novel approach to address these limitations.
Purpose of the Study:
- To demonstrate the generic nature and broad applicability of the IMPC methodology.
- To integrate IMPC with established advanced control schemes.
- To evaluate the performance improvements offered by IMPC-enhanced controllers.
Main Methods:
- Developed the Infinite Model Predictive Control (IMPC) algorithm for nonlinear systems.
- Implemented IMPC with PI controller (Smith-Predictor), Dahlin controller, SPC, DMC, and m-DMC.
- Conducted experimental and simulation studies on SISO and MIMO systems, including real-world applications.
Main Results:
- IMPC integration significantly improved control performance across various advanced control strategies.
- The IMPC methodology proved readily implementable on diverse control schemes.
- Enhanced controllers demonstrated superior performance compared to their original forms in simulations and practical applications.
Conclusions:
- The IMPC strategy is a versatile and effective tool for enhancing existing advanced control systems.
- IMPC provides a generic framework for improving control performance in complex industrial processes.
- The study validates IMPC's potential for real-time applications in sectors like manufacturing and thermal systems.
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