# 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

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topJiamei 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 -

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