Wiener model based GMVC design considering sensor noise and delay.

Area of Science:

• Control Engineering
• Process Control
• Nonlinear Systems

Background:

• Traditional linear control loop performance assessment methods are inadequate for nonlinear systems.
• Real-world applications face challenges from sensor noise and delays, limiting control performance.
• Existing techniques often fail to address these practical limitations effectively.

Purpose of the Study:

• To propose a novel nonlinear generalized minimum variance (NGMV) controller for single input-single output (SISO) Wiener models.
• To establish the NGMV controller's performance as a benchmark for nonlinear control loops.
• To develop a method capable of online parameter estimation for nonlinear models.

Main Methods:

• Utilized a single input-single output (SISO) Wiener model for controller design.
• Implemented recursive least squares (RLS) for online parameter estimation of the nonlinear model.
• Incorporated k-step ahead prediction to mitigate sensor delay effects.
• Applied an exponential digital filter to reduce measurement noise impact.

Main Results:

• The proposed NGMV controller demonstrates effective performance for nonlinear control loops.
• Online parameter estimation of the nonlinear model was successfully achieved using RLS.
• The methods effectively handled sensor delay and measurement noise, improving control performance.
• Simulation on a pH neutralization process validated the controller's effectiveness.

Conclusions:

• The NGMV controller provides a robust solution for assessing and improving nonlinear control loop performance.
• The developed approach effectively addresses practical limitations such as sensor delay and noise.
• This method offers a valuable benchmark and practical tool for nonlinear process control applications.