Model Order Reduction based on Agglomerative Hierarchical Clustering
Abstract
This paper presents an improved method for reducing high-order dynamical system models via clustering. Agglomerative hierarchical clustering based on performance evaluation (HC-PE) is introduced for model order reduction. This method computes the reduced order denominator of the transfer function model by clustering system poles in a hierarchical dendrogram. The base layer represents an nth order system, which is used to calculate each successive layer to reduce the model order until finally reaching a second-order system. HC-PE uses a mean-squared error (MSE) in every reduced order, which modifies the pole placement process. The coefficients for the numerator of the reduced model are calculated by using the Padé approximation (PA) or alternatively a genetic algorithm (GA). Several numerical examples of reducing techniques are taken from the literature to compare with HC-PE. Two classes of results are shown in this paper. The first sets are single-input single-output models that range from simple models to 48th order systems. The second sets of experiments are with a multi-input multioutput model. We demonstrate the best performance for HC-PE through minimum MSEs compared with other methods. Furthermore, the robustness of HC-PE combined with PA or GA is confirmed by evaluating the third-order reduced model for the triple-link inverted pendulum model by adding a disturbance impulse signal and by changing model parameters. The relevant stability proofs are provided in Appendixes A and B in the supplementary material. HC-PE with PA slightly outperforms its performance with GA, but both approaches are attractive alternatives to other published methods.
Recommended Citation
S. Al-Dabooni and D. C. Wunsch, "Model Order Reduction based on Agglomerative Hierarchical Clustering," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 6, pp. 1881 - 1895, Institute of Electrical and Electronics Engineers (IEEE), Jun 2019.
The definitive version is available at https://doi.org/10.1109/TNNLS.2018.2873196
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Genetic Algorithm (GA); Hierarchical Clustering; Linear Quadratic Regulator; Model Order Reduction (MOR); Padé Approximation (PA); Pole Replacement; Triple-Link Inverted Pendulum
International Standard Serial Number (ISSN)
2162-237X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 2019
Comments
This research was sponsored by the Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center, and the Army Research Laboratory (ARL), and was accomplished under Cooperative Agreement Number W911NF-18-2-0260, the Higher Committee for Educational Development (HCED), and the Basrah Oil Company (BOC) in Iraq.