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Related Experiment Video

Updated: Feb 1, 2026

Synthesis, Cellular Delivery and In vivo Application of Dendrimer-based pH Sensors
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Wiener model based GMVC design considering sensor noise and delay.

Mahdi Kazemi1, Mohammad Mehdi Arefi1, Yousef Alipouri2

  • 1Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

ISA Transactions
|December 18, 2018
PubMed
Summary

A new nonlinear generalized minimum variance (NGMV) controller is proposed for nonlinear control loops, offering online parameter estimation. This method addresses real-world challenges like sensor delay and noise, outperforming traditional techniques.

Keywords:
Nonlinear generalized minimum variance control (NGMV)Sensor and measurement delayWiener modelpH neutralization process

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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.