Offset-free ARX-based adaptive model predictive control applied to a nonlinear process.

Area of Science:

• Process Control
• System Identification
• Chemical Engineering

Background:

• Nonlinear process control often faces challenges with model mismatch and varying operating conditions.
• Achieving zero-offset tracking and robustness is crucial for industrial applications.

Purpose of the Study:

• To develop an adaptive model predictive control (MPC) strategy for nonlinear process regulation.
• To achieve robust setpoint tracking with zero steady-state error, even with model uncertainty.

Main Methods:

• Development of a steady-state constrained autoregressive with extra input (SSARX) model identification method.
• Integration of the SSARX model into a linear MPC framework.
• Design of an information matrix-based index for online model re-identification.
• Adaptive adjustment of control parameters for varying operating conditions.

Main Results:

• The proposed linear MPC framework effectively minimizes output deviation from the setpoint.
• Zero-offset control is achieved, demonstrating robustness against model mismatch.
• The adaptive algorithm successfully tracks setpoints across a wide range of operating conditions.

Conclusions:

• The adaptive SSARX-MPC approach provides an effective solution for regulating nonlinear processes.
• The method offers robust and accurate setpoint tracking in dynamic industrial environments.
• Demonstrated effectiveness in a fermenter control case study.