Structurally optimized neural fuzzy modelling for model predictive control

Hu, X., Gong, Y., Zhao, D. and Gu, W. (2023) Structurally optimized neural fuzzy modelling for model predictive control. IEEE Transactions on Industrial Informatics, 19(6), pp. 7498-7507. (doi: 10.1109/TII.2021.3133893)

Abstract

This paper investigates the local linear model tree (LOLIMOT), a typical neural fuzzy model, in the multiple-input-multiple-output model predictive control (MPC). In the conventional LOLIMOT, the structural parameters including centres and variances of its Gaussian kernels are set based on equally dividing the input data space. In this paper, after the structural parameters are initially obtained from the input space partition, they are optimized by the gradient descent search, from which the space partitions are further adjusted. This makes it better for the model structure to fit the input data statistics, leading to improved modelling performance with small model size. The MPC based on the proposed structurally optimized LOLIMOT is then implemented and verified with both numerical and diesel engine plants. Validation results show that the proposed MPC has significantly better controlling performance than the MPC based on the conventional LOLIMOT, making it an attractive solution in practice.

• Item Type: Articles
• Additional Information: This work was supported by the Engineering and Physical Sciences Research Council of the U.K., through the EPSRC Innovation Fellowship, under Grant EP/S001956/2.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Hu, X., Gong, Y., Zhao, D., and Gu, W.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Industrial Informatics
• Publisher: IEEE
• ISSN: 1551-3203
• ISSN (Online): 1941-0050
• Published Online: 09 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Industrial Informatics 19(6): 7498-7507
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314774		Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learning	Dezong Zhao	Engineering and Physical Sciences Research Council (EPSRC)	EP/S001956/2	ENG - Autonomous Systems & Connectivity