Input constraints handling in an MPC/feedback linearization scheme

Jiamei Deng; Victor M. Becerra; Richard Stobart

International Journal of Applied Mathematics and Computer Science (2009)

  • Volume: 19, Issue: 2, page 219-232
  • ISSN: 1641-876X

Abstract

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The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

How to cite

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Jiamei Deng, Victor M. Becerra, and Richard Stobart. "Input constraints handling in an MPC/feedback linearization scheme." International Journal of Applied Mathematics and Computer Science 19.2 (2009): 219-232. <http://eudml.org/doc/207929>.

@article{JiameiDeng2009,
abstract = {The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.},
author = {Jiamei Deng, Victor M. Becerra, Richard Stobart},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {predictive control; feedback linearization; neural networks; nonlinear systems; constraints},
language = {eng},
number = {2},
pages = {219-232},
title = {Input constraints handling in an MPC/feedback linearization scheme},
url = {http://eudml.org/doc/207929},
volume = {19},
year = {2009},
}

TY - JOUR
AU - Jiamei Deng
AU - Victor M. Becerra
AU - Richard Stobart
TI - Input constraints handling in an MPC/feedback linearization scheme
JO - International Journal of Applied Mathematics and Computer Science
PY - 2009
VL - 19
IS - 2
SP - 219
EP - 232
AB - The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
LA - eng
KW - predictive control; feedback linearization; neural networks; nonlinear systems; constraints
UR - http://eudml.org/doc/207929
ER -

References

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Citations in EuDML Documents

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  1. Jimoh Olarewaju Pedro, Aarti Panday, Laurent Dala, A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling
  2. Jimoh Olarewaju Pedro, Olurotimi Akintunde Dahunsi, Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system
  3. Marlene Arangú, Miguel A. Salido, A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems
  4. Maciej Ławryńczuk, Nonlinear state-space predictive control with on-line linearisation and state estimation

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