Results on Tensor Product-based Model Transformation of Magnetic Levitation Systems

Hedrea, Elena‐Lorena; Precup, Radu‐Emil; Bojan-Dragoş, Claudia-Adina

doi:10.12700/aph.16.9.2019.9.6

Cited by 26 publications

(1 citation statement)

References 42 publications

(59 reference statements)

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“…Figure 1 depicts the laboratory process control network, which consists of several control processes, namely: a heating-cooling test stand (slow, simple, stable, continuous process) [ 58 ], MPS Festo stand (fast, complex, stable, binary process) [ 59 , 60 , 61 ], and a magnetic levitation (MAGLEV) process using INTECO (quick, simple, unstable, continuous process) [ 62 , 63 ]. Out of those, only the magnetic levitation process is considered in this paper.…”

Section: Laboratory Process Control Networkmentioning

confidence: 99%

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

Zarzycki,

Chaber,

Cabaj

et al. 2023

Sensors

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This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.

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Section: Laboratory Process Control Networkmentioning

confidence: 99%

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

Zarzycki,

Chaber,

Cabaj

et al. 2023

Sensors

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Cyclical inverse interpolation: An approach for the inverse interpolation of black‐box models using tensor product representations

Csapó

2021

Asian Journal of Control

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This paper proposes a novel approach to inverse interpolating black‐box models, referred to as the cyclical inverse interpolation method (CIIM). The approach relies on the use of a multivariate surrogate function, expressed as a tensor product (TP) model, to systematically generate candidate inputs to the given black‐box model with the goal of obtaining interpolated outputs. While the proposed approach is largely agnostic as to the form of this surrogate function, some of its properties, such as the semantics of its input dimensions with respect to the black box model, are constructively defined. The paper demonstrates the viability of the proposed approach both from a theoretical perspective and through numerical examples. Based on these results, it is argued that the approach can be used for the exploratory identification of black‐box models that have scalar‐valued outputs and can be particularly useful in working with black‐box models that have a large number of inputs and exhibit highly nonlinear behavior.

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Tensor product model transformation‐based control for fractional‐order biological pest control systems

Boonyaprapasorn

Kuntanapreeda

Ngiamsunthorn

et al. 2020

Asian Journal of Control

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As environmental pollution and safety of human and creatures have been an issue of concern, pest management based on nonchemical use is preferable. Biological pest control can satisfy the regulation of pest population without causing environmental problems. Moreover, representation of dynamical systems in the form of fractional‐order dynamic models can explain several phenomena better than the integer‐order dynamic models can. The aim of this study is to determine the biological pest control policy for the fractional‐order pest control systems, which is a complex nonlinear multiple input and multiple output control problem. Here, the system is represented in the form of a fractional‐order Lotka–Volterra model. In order to avoid complexity in the nonlinear feedback controller design process, the tensor product (TP) model transformation was applied to automatically transform the biological pest control system to the TP polytopic model which allows the designer to synthesize the controller under linear framework. Moreover, the design process can be easily carried out by solving an linear matrix inequality (LMI) problem, which is very well applicable to high‐dimensional multiple input and multiple output control problems. Simulation studies were conducted to demonstrate the ease of the TP‐based design process. The simulation results showed that the designed control policy effectively manipulates the pest populations of the fractional‐order Lotka–Volterra model to the desired level which is economically viable.

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Results on Tensor Product-based Model Transformation of Magnetic Levitation Systems

Cited by 26 publications

References 42 publications

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

Cyclical inverse interpolation: An approach for the inverse interpolation of black‐box models using tensor product representations

Tensor product model transformation‐based control for fractional‐order biological pest control systems

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