Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input...

Area of Science:

• Control Engineering
• Applied Mathematics
• System Dynamics

Background:

• Nonlinear processes often face challenges from external disturbances and operational constraints.
• Model predictive control (MPC) offers a framework for handling such complexities, but requires accurate models.
• Fuzzy logic systems provide a powerful tool for modeling and controlling nonlinear systems.

Purpose of the Study:

• To develop a robust control scheme for nonlinear processes affected by disturbances and input constraints.
• To integrate a disturbance observer with fuzzy model predictive control (FMPC) for enhanced performance.
• To systematically determine optimal linearization points for accurate fuzzy model creation.

Main Methods:

• A Takagi-Sugeno fuzzy model is employed to represent the nonlinear process.
• A disturbance observer is designed to estimate and compensate for system disturbances.
• Fuzzy model predictive control (FMPC) is implemented, incorporating stability proofs and constraint satisfaction.
• A systematic approach using the gap metric is used for selecting linearization points.

Main Results:

• The proposed disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme demonstrates effective disturbance rejection.
• Asymptotic stability of the FMPC is theoretically proven, ensuring reliable system operation.
• Input constraints are successfully managed throughout the control process.
• The composite DOBFMPC law effectively removes disturbance influence from output channels at steady state.

Conclusions:

• The DOBFMPC scheme provides a robust and effective solution for controlling nonlinear systems with disturbances and constraints.
• The systematic approach to fuzzy model creation enhances control accuracy and complexity management.
• The method is validated through successful application to a boiler-turbine system, showcasing practical viability.