Generalized Discrete-time nonlinear disturbance observer based fuzzy model predictive control for boiler-turbine...

Area of Science:

• Control Systems Engineering
• Power Plant Automation
• Nonlinear System Control

Background:

• Boiler-turbine systems (BTS) present significant control challenges due to tight input constraints, strong nonlinearities, and complex disturbances.
• Existing control methods often struggle to simultaneously address these multifaceted issues, impacting system performance and stability.
• Effective control strategies are crucial for optimizing the efficiency and reliability of power generation.

Purpose of the Study:

• To propose a novel disturbance observer based fuzzy model predictive control (DOBFMPC) scheme for boiler-turbine systems (BTS).
• To enhance disturbance estimation capabilities for higher-order disturbances.
• To ensure asymptotic stability and satisfy input constraints in the presence of uncertainties.

Main Methods:

• Development of a generalized discrete-time nonlinear disturbance observer (GDNDO) for higher-order disturbance estimation.
• Synthesis of a baseline fuzzy model predictive control (FMPC) law based on a fuzzy model.
• Integration of GDNDO estimates with FMPC to form the composite DOBFMPC law.

Main Results:

• The GDNDO precisely estimates disturbances when its order matches or exceeds the disturbance order.
• The FMPC guarantees asymptotic stability and enforces input constraints through state feedback.
• The DOBFMPC scheme effectively removes disturbance influence from output channels at steady state.

Conclusions:

• The proposed DOBFMPC scheme offers a robust and effective solution for controlling complex boiler-turbine systems.
• The integration of advanced disturbance observation and predictive control significantly improves system performance.
• Simulations on a 300 MW subcritical BTS validate the efficacy of the developed control strategy.