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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.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Time-Domain Interpretation of PD Control01:07

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.
Consider the example of control of motor torque. Initially, a positive...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
PD Controller: Design01:26

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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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.