2022
DOI: 10.1002/cjce.24398
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A comparative study of model approximation methods applied to economicMPC

Abstract: Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for next‐generation smart manufacturing. It has the potential to not only improve economic performance but also significantly increase computational complexity. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation met… Show more

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Cited by 9 publications
(15 citation statements)
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“…A multi‐level pseudorandom signal is similar to a pseudorandom binary signal, except that it has multiple levels, which helps to stimulate the nonlinear dynamics of the agro‐hydrological system. More details regarding the design of multi‐level pseudorandom inputs can be found in our previous work 22 . Figure 3 presents a segment of the training input signal and the corresponding system output for M2$$ {M}_2 $$.…”
Section: Proposed Two‐layer Neural Networkmentioning
confidence: 99%
See 1 more Smart Citation
“…A multi‐level pseudorandom signal is similar to a pseudorandom binary signal, except that it has multiple levels, which helps to stimulate the nonlinear dynamics of the agro‐hydrological system. More details regarding the design of multi‐level pseudorandom inputs can be found in our previous work 22 . Figure 3 presents a segment of the training input signal and the corresponding system output for M2$$ {M}_2 $$.…”
Section: Proposed Two‐layer Neural Networkmentioning
confidence: 99%
“…TensorFlow 27 is used to train the NN models. Readers may refer to our previous work 22 for more details regarding data pre‐processing and NN training.…”
Section: Proposed Two‐layer Neural Networkmentioning
confidence: 99%
“…A multi-level pseudorandom signal is similar to a pseudorandom binary signal, except that it has multiple levels, which helps to stimulate the nonlinear dynamics of the agro-hydrological system. More details regarding the design of multilevel pseudorandom inputs can be found in our previous work [18]. Figure 3 presents a segment of the training input signal and the corresponding system output for M 2 .…”
Section: Design and Training Of First-layer Nnsmentioning
confidence: 99%
“…TensorFlow [23] is used to train the NN models. Readers may refer to our previous work [18] for more details regarding data pre-processing and NN training.…”
Section: Design and Training Of First-layer Nnsmentioning
confidence: 99%
See 1 more Smart Citation