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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
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The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
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Neural network identification in nonlinear model predictive control for frequent and infrequent operating points

Saman Saki1, Alireza Fatehi1

  • 1APAC Research Group, Industrial Control Center of Excellence, K.N. Toosi University of Technology, Tehran, Iran.

ISA Transactions
|August 24, 2019
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Summary

This study introduces an optimal identification algorithm for nonlinear model predictive control (NMPC) to improve performance in dynamic processes. The method enhances model accuracy across frequent and infrequent operating points, ensuring stability and reducing tracking errors.

Keywords:
Advanced process controlNonlinear identificationNonlinear model predictive controlNonlinearity measureVolterra series

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Area of Science:

  • Chemical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Nonlinear Model Predictive Control (NMPC) struggles with highly nonlinear dynamic processes due to model inaccuracies at infrequent operating points (IOPs).
  • Existing nonlinear models, identified from frequent operating points (FOPs), lead to tracking errors and potential instability when processes deviate.
  • Dynamic processes often exhibit short-term shifts from FOPs to IOPs, challenging traditional control strategies.

Purpose of the Study:

  • To develop a novel optimal identification algorithm for training NMPC nonlinear models.
  • To enhance the accuracy and robustness of NMPC in highly nonlinear dynamic systems, particularly during transitions to IOPs.
  • To ensure stable and precise control across both FOPs and IOPs.

Main Methods:

  • A novel optimal identification algorithm is proposed, tailored to the plant's nonlinearity.
  • A Multi-Layer Perceptron (MLP) neural network is employed as the nonlinear model.
  • The MLP is trained to accurately represent system behavior at FOPs while maintaining acceptable performance at IOPs.

Main Results:

  • The proposed algorithm successfully trains an MLP to describe nonlinear dynamic system behavior accurately.
  • The trained model demonstrates acceptable performance even in infrequent operating points.
  • Validation on a nonlinear dynamic pH neutralization process shows significant improvements in simulation and implementation.

Conclusions:

  • The developed optimal identification algorithm effectively addresses NMPC challenges in highly nonlinear dynamic processes.
  • The method improves model accuracy and control system stability across varying operating conditions.
  • Simulation and implementation results confirm the algorithm's practical effectiveness and robustness.