Autor:

Deng, Jiamei ; Becerra, Victor M. ; Stobart, Richard

Współtwórca:

Korbicz, Józef (1951- ) - ed.

Tytuł:

Input constraints handling in an MPC/feedback linearization scheme

Tytuł publikacji grupowej:

AMCS, volume 19 (2009)

Temat i słowa kluczowe:

predictive control ; feedback linearization ; neural networks ; nonlinear systems ; constraints

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2009

Typ zasobu:

artykuł

DOI:

10.2478/v10006-009-0018-2

Strony:

219-232

Źródło:

AMCS, volume 19, number 2 (2009) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego