Multi-linear model set design based on the nonlinearity measure and H-gap metric.
Davood Shaghaghi1, Alireza Fatehi1, Ali Khaki-Sedigh1
1APAC Research Group, Industrial Control Center of Excellence, Faculty of Electrical Engineering, K. N. Toosi University of Technology, 16317-14191 Tehran, Iran.
ISA Transactions
|February 14, 2017
Summary
This study introduces a new model bank selection method for nonlinear systems. It reduces model switching and computational load for improved control system performance.
Area of Science:
- Control Engineering
- Systems Science
Background:
- Nonlinear systems with wide operating ranges present challenges for traditional control methods.
- Designing effective model banks for complex systems requires robust selection algorithms.
Purpose of the Study:
- To propose an efficient model bank selection method for nonlinear systems.
- To integrate this method with model predictive controllers for advanced process control.
- To reduce computational complexity and model switching in control systems.
Main Methods:
- Utilized a nonlinearity measure and the H-gap metric for model bank design.
- Developed an algorithm for selecting appropriate models within the bank.
- Integrated the model bank with model predictive controllers (MPC).
Main Results:
- Demonstrated a reduction in excessive model switching.
- Showcased a decrease in computational complexity within the controller bank.
- Verified improved control system performance through simulations and experimental studies.
Conclusions:
- The proposed model bank selection method is effective for nonlinear systems.
- The integration with MPC leads to high-performance advanced process control.
- The algorithm offers significant advantages in terms of efficiency and performance.
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