Creator:
Deng, Jiamei ; Becerra, Victor M. ; Stobart, Richard
Contributor:
Title:
Input constraints handling in an MPC/feedback linearization scheme
Group publication title:
Subject and Keywords:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
DOI:
Pages:
Source:
AMCS, volume 19, number 2 (2009) ; click here to follow the link