<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Input Constraints Handling in an MPC/Feedback Linearization Scheme

Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU, UK

School of Systems Engineering, University of Reading, Reading, RG6 6AY, UK

International Journal of Applied Mathematics and Computer Science

{"article-title":"Input Constraints Handling in an MPC/Feedback Linearization Scheme"}

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving...

Input Constraints Handling in an MPC/Feedback Linearization Scheme

Online veröffentlicht: 08. Juli 2009

Seitenbereich: 219 - 232

DOI: https://doi.org/10.2478/v10006-009-0018-2

Schlüsselwörter
predictive control, feedback linearization, neural networks, nonlinear systems, constraints

This content is open access.

Input Constraints Handling in an MPC/Feedback Linearization Scheme

Online veröffentlicht: 08. Juli 2009

Seitenbereich: 219 - 232

DOI: https://doi.org/10.2478/v10006-009-0018-2

Schlüsselwörterpredictive control, feedback linearization, neural networks, nonlinear systems, constraints

This content is open access.

Schlüsselwörter
predictive control, feedback linearization, neural networks, nonlinear systems, constraints