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A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for

Bo Wang1, Muhammad Shahzad1, Xianglin Zhu1

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)
|June 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Grey-Wolf Optimization (GWO) to enhance L-Lysine production control. GWO optimizes a Least-Square Support Vector Machine (LSSVM) for accurate real-time prediction, improving non-linear model predictive control (NMPC) performance.

Keywords:
L-Lysine fermentationgrey-wolf optimizationleast-square support vector machinemachine learningmodel predictive control

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Area of Science:

  • Biochemical Engineering
  • Process Control
  • Optimization Algorithms

Background:

  • L-Lysine production relies on complex non-linear fermentation processes.
  • Real-time control of L-Lysine concentration is crucial for enhancing yield but faces measurement challenges.
  • Accurate real-time prediction of product concentration is needed for effective process control.

Purpose of the Study:

  • To develop an advanced control strategy for real-time L-Lysine production enhancement.
  • To improve the accuracy and adaptability of predictive models for fermentation processes.
  • To optimize the non-linear model predictive control (NMPC) using advanced algorithms.

Main Methods:

  • Implemented a Least-Square Support Vector Machine (LSSVM) for real-time product concentration prediction.
  • Utilized Grey-Wolf Optimization (GWO) to optimize LSSVM parameters (penalty factor, kernel width), creating GWO-LSSVM.
  • Applied GWO to solve the optimization problems within the NMPC framework, resulting in GWO-NMPC.
  • Compared GWO-based methods against Particle Swarm Optimization (PSO)-based counterparts (PSO-LSSVM and PSO-NMPC).

Main Results:

  • The GWO-LSSVM demonstrated superior prediction accuracy and real-time tracking ability compared to PSO-LSSVM.
  • GWO-NMPC exhibited enhanced adaptability, reduced overall error, and improved control precision over PSO-NMPC.
  • The integrated GWO-based approach significantly improved L-Lysine production control.

Conclusions:

  • Grey-Wolf Optimization provides a robust method for optimizing predictive models and control strategies in complex fermentation processes.
  • The proposed GWO-LSSVM and GWO-NMPC system offers a significant advancement for real-time control and yield enhancement in L-Lysine production.
  • GWO-based predictive control outperforms PSO-based methods in terms of accuracy, adaptability, and precision for this application.