Advertisement

Nonlinear Predictive Neural Control

  • G. P. Liu
Chapter
  • 606 Downloads
Part of the Advances in Industrial Control book series (AIC)

Abstract

Predictive control is now widely used by industry and a large number of implementation algorithms, including generalised predictive control (Clarke et al., 1987), dynamic matrix control (Cutler and Ramaker, 1980), extended prediction self-adaptive control (Keyser and Cauwenberghe, 1985), predictive function control (Richalet et al., 1987), extended horizon adaptive control (Ydstie, 1984) and unified predictive control (Soeterboek et al., 1990), have appeared in the literature. Most predictive control algorithms are based on a linear model of the process. However, industrial processes usually contain complex nonlinearities and a linear model may be acceptable only when the process is operating around an equilibrium point. If the process is highly nonlinear, a nonlinear model will be necessary to describe the behaviour of the process.

Keywords

Reference Trajectory Reference Input Prediction Horizon Radial Basis Functi Cont Roller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • G. P. Liu
    • 1
  1. 1.School of Mechanical Materials, Manufacturing Engineering and ManagementUniversity of NottinghamNottinghamUK

Personalised recommendations