2022
DOI: 10.1109/lcsys.2021.3077201
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Structured Hammerstein-Wiener Model Learning for Model Predictive Control

Abstract: This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-convex and difficult to solve online. In this paper, we propose a model that combines the Hammerstein-Wiener model with input convex neural networks, which have recently been proposed in the field of machine learning. An important feature of the proposed model is that resulting optimal control problems are effectively solvable exploi… Show more

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Cited by 7 publications
(4 citation statements)
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“…Therefore, the predictive control method of nonlinear Wiener model is very important. The Wiener model describes a special type of nonlinear model that consists of a dynamic linear module and a steady‐state nonlinear module 32 . The schematic diagram of the model is shown in Figure 2, where hfalse(·false)$$ h\left(\cdotp \right) $$ represents a static nonlinear block.…”
Section: Establish Inlet Temperaturementioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, the predictive control method of nonlinear Wiener model is very important. The Wiener model describes a special type of nonlinear model that consists of a dynamic linear module and a steady‐state nonlinear module 32 . The schematic diagram of the model is shown in Figure 2, where hfalse(·false)$$ h\left(\cdotp \right) $$ represents a static nonlinear block.…”
Section: Establish Inlet Temperaturementioning
confidence: 99%
“…The Wiener model describes a special type of nonlinear model that consists of a dynamic linear module and a steady-state nonlinear module. 32 The schematic diagram of the model is shown in Figure 2, where h(⋅) represents a static nonlinear block.…”
Section: Wiener Modelmentioning
confidence: 99%
“…The Hammerstein model constitutes a static nonlinear block followed by linear dynamic block and the Wiener model comprises of a linear dynamic cascaded with a static nonlinearity [4] . The union of both the Hammerstein and the Wiener system generates the Hammerstein-Wiener model, which consists of one dynamic linear block sandwiched between two static nonlinear blocks having applications in all fields of science and engineering including nonlinear industrial processes [5] , controls [6] , signal processing [7] , and instrumentation [8] . Several parameter estimation procedures have been formulated for identification of the Hammerstein-Wiener model, mainly including one-shot set-membership method [9] , subspace method [10] , blind approach [11] , over parametrization [12] , recursive least square algorithm [13] , maximum likelihood method [14] iterative method [15] multi-signal-based method [16] and fractional approach [17] .…”
Section: Introductionmentioning
confidence: 99%
“…In a related attempt, modeling based on the Hammerstein-Wiener model has been done in [5]. However, its internal dynamics have been limited to linear, and the model has not been structurally guaranteed to be stabilizable.…”
Section: Introductionmentioning
confidence: 99%