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Related Concept Videos

Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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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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Open and closed-loop control systems01:17

Open and closed-loop control systems

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Related Experiment Video

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Nonlinear model identification and adaptive model predictive control using neural networks.

Vincent A Akpan1, George D Hassapis

  • 1Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. vin_ava@yahoo.co.uk

ISA Transactions
|February 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces two adaptive model predictive control algorithms for complex systems. The first algorithm, Nonlinear Model Predictive Control, demonstrates superior performance in furnace and aircraft control compared to Generalized Predictive Control, despite higher computational demands.

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Experimental Methods to Study Human Postural Control
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Experimental Methods to Study Human Postural Control

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Last Updated: Jun 4, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Process Systems Engineering

Background:

  • Adaptive control is crucial for dynamic systems like fluidized bed furnace reactors and aircraft autopilots.
  • Model predictive control (MPC) offers advanced control strategies but requires accurate process models.
  • Online process identification is essential for adaptive MPC to handle system uncertainties and variations.

Purpose of the Study:

  • To develop and evaluate two novel adaptive model predictive control algorithms.
  • To compare the performance of Nonlinear Model Predictive Control (NMPC) against Generalized Predictive Control (GPC) in adaptive settings.
  • To assess the effectiveness of using neural networks with recursive least squares for online process identification within adaptive MPC.

Main Methods:

  • Implementation of two adaptive MPC algorithms, each with an online identification module and a predictive control module.
  • Utilizing a series-parallel neural network trained via an recursive least squares (ARLS) method for process model approximation.
  • Applying the algorithms to temperature control of a fluidized bed furnace reactor (FBFR) and F-16 aircraft auto-pilot control simulations.

Main Results:

  • Both adaptive MPC algorithms were successfully implemented and simulated on FBFR and F-16 models.
  • The NMPC-based algorithm demonstrated superior control performance compared to the GPC-based algorithm.
  • The enhanced performance of the NMPC algorithm came at the cost of increased computational time.

Conclusions:

  • Adaptive MPC strategies integrating online neural network identification are effective for complex control tasks.
  • NMPC offers improved control accuracy over GPC in the presented adaptive framework.
  • The trade-off between computational cost and control performance should be considered when selecting adaptive MPC algorithms.