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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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Related Experiment Videos

A Novel Data-Driven Algorithm for Prediction Horizon Estimation in Model Predictive Control.

Bojan Jorgovanović1,2, Nikola Jorgovanović1,2, Darko Stanišić2

  • 1Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a new method for tuning model predictive control (MPC) by estimating the prediction horizon using LSTM-simulated data. This offline approach enhances control performance and reduces computational load in industrial processes.

Keywords:
closed-loop controlcross-correlation analysisdata-driven modellingdata-driven tuninglong short-term memorymodel predictive controlprediction horizon estimation

Related Experiment Videos

Area of Science:

  • Process Control
  • Artificial Intelligence in Engineering
  • System Identification

Background:

  • Model Predictive Control (MPC) is crucial in industrial applications, but its tuning, particularly the prediction horizon, lacks systematic methods.
  • The prediction horizon significantly impacts MPC performance and computational requirements.

Purpose of the Study:

  • To develop a novel, systematic algorithm for estimating the prediction horizon in MPC.
  • To enable offline determination of the prediction horizon, reducing reliance on physical system experiments.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) networks to create data-driven models of controlled processes.
  • Employed cross-correlation analysis on LSTM-simulated input-output data for prediction horizon estimation.
  • Integrated the estimated horizons into an MPC framework for closed-loop simulations.

Main Results:

  • The proposed algorithm successfully estimated prediction horizons for two benchmark systems (continuous stirred-tank reactor, single tank system).
  • MPC controllers tuned with the estimated horizons demonstrated strong control performance in simulations.
  • LSTM-based simulation eliminated the need for physical system testing, enabling offline configuration.

Conclusions:

  • Cross-correlation analysis of LSTM-simulated data offers a reliable and systematic approach to MPC prediction horizon estimation.
  • The developed method provides a practical tool for optimizing MPC tuning in industrial process control.
  • This offline approach enhances the efficiency and effectiveness of MPC implementation.