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

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.