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Article

The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects

1
Institute of Control and Computation Engineering, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warszawa, Poland
2
Emerson Process Management, 200 Beta Dr, Pittsburgh, PA 15238, USA
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(8), 178; https://doi.org/10.3390/a13080178
Received: 2 June 2020 / Revised: 17 July 2020 / Accepted: 20 July 2020 / Published: 23 July 2020
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry. View Full-Text
Keywords: high order model; model predictive control (MPC); generalized predictive control (GPC); numerical problems high order model; model predictive control (MPC); generalized predictive control (GPC); numerical problems
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MDPI and ACS Style

Plamowski, S.; Kephart, R.W. The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects. Algorithms 2020, 13, 178. https://doi.org/10.3390/a13080178

AMA Style

Plamowski S, Kephart RW. The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects. Algorithms. 2020; 13(8):178. https://doi.org/10.3390/a13080178

Chicago/Turabian Style

Plamowski, Sebastian, and Richard W. Kephart 2020. "The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects" Algorithms 13, no. 8: 178. https://doi.org/10.3390/a13080178

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