Nonparametric nonlinear model predictive control

Kashiwagi, H. and Li, Y. (2004) Nonparametric nonlinear model predictive control. Korean Journal of Chemical Engineering, 21(2), pp. 329-337. (doi: 10.1007/BF02705416)

Abstract

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Item Type: Articles
• Keywords: Model predictive control, process control, nonlinear modeling, volterra kernels, M-sequence.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Kashiwagi, H., and Li, Y.
• Subjects: T Technology > T Technology (General)
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Research Group: Centre for Systems and Control
• Journal Name: Korean Journal of Chemical Engineering
• Publisher: Springer
• ISSN: 1975-7220
• Copyright Holders: Copyright © 2004 Springer
• First Published: First published in Korean Journal of Chemical Engineering 21(2):329-337
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.

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