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Data-Driven Control of Nonlinear Process Systems Using a Three-Degree-of-Freedom Model-on-Demand Model Predictive

Sarasij Banerjee1, Owais Khan1, Mohamed El Mistiri1

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Summary
This summary is machine-generated.

This study introduces a Model-on-Demand (MoD) approach integrated with three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) for enhanced system identification and control of nonlinear systems.

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Area of Science:

  • Control Engineering
  • System Identification
  • Nonlinear Systems

Background:

  • Conventional Model Predictive Control (MPC) algorithms often require complex tuning. System identification methods can be computationally demanding.
  • Existing methods may struggle with nonlinear and complex systems, limiting performance and ease of use.

Purpose of the Study:

  • To present a novel Model-on-Demand (MoD) estimation approach integrated with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework.
  • To demonstrate improved performance and intuitive tuning for both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems.
  • To provide a computationally efficient and robust method for controlling complex nonlinear process systems.

Main Methods:

  • Developed a hybrid MoD estimation technique combining local and global modeling for computational efficiency.
  • Integrated MoD-based predictive models into the 3DoF-KF MPC framework for adaptive control actions.
  • Validated the approach using case studies on an epidemic SIR model and a nonlinear MIMO CSTR model.

Main Results:

  • The MoD-based 3DoF-KF MPC framework achieved notable performance enhancements over conventional ARX models for both SISO and MIMO control.
  • Demonstrated superior performance and robustness in controlling a nonlinear MIMO Continuous Stirred Tank Reactor (CSTR) model.
  • Provided guidelines for designing effective databases for MoD-based MIMO identification and control implementation.

Conclusions:

  • The synergistic integration of MoD estimation and 3DoF-KF MPC offers significant technological and practical improvements for system identification and control.
  • This approach provides an effective strategy for operating complex nonlinear process systems with enhanced performance and ease of use.
  • The MoD-based 3DoF-KF MPC framework represents a computationally efficient and robust solution for advanced process control.