Evaluation of Nonlinear ARX System Identification Technique on Modeling Crosstalk

Sudrajat, Muhammad Imam; Wibisono, Muhammad Ammar; Loschi, Hermes; Moonen, Niek; Leferink, Frank

doi:10.1109/emcsi39492.2022.9889401

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…turn-on duration (see Figure 5.6-C). Furthermore, as presented by [144] the highest CE at f 0 =100 kHz occurs when the D≈50%. The same principle, can be seen in Figure A.8 of the subsection A.2, and in the Figure 5.9 of the section 5.3.…”

Section: Conducted Emission Behaviourmentioning

confidence: 77%

“…The same principle, can be seen in Figure A.8 of the subsection A.2, and in the Figure 5.9 of the section 5.3. Figure 5.7, also considered in [144], indicates that the D effects at the f 0 , is not linear but resembles a quadratic function. It is noteworthy that the effects presented by Moreover, according to the measurement data (blue line) presented by Figure 5.7.…”

Section: Conducted Emission Behaviourmentioning

confidence: 95%

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Modulation Algorithms of Power Electronic Converters for Shaping of Conducted Interference

Loschi¹

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Remarkably, over the last few decades, the electromagnetic interference within 9 to 150 kHz was especially accentuated since the switching frequency of power electronic converters is still limited to several kHz. Thus, bringing up the debate on regulation. From the point of view of the electromagnetic interference noise shape, two cases represent the extremes of possibilities among the various possible converter control algorithms: the deterministic and pseudo-random modulation.Pseudo-random modulation has been considered a complementary methodology to achieve electromagnetic compatibility, jointly with the electromagnetic interference filters and improvements on the printed circuit board layouts. The pseudorandom modulation methodology has also been utilized as prior-art for the reduction of converter power losses while achieving a target control dynamics or system power density or cost, and/or to achieve a flattening of power efficiency curves as function of processed power, and to improve control dynamics in transient operations. Notwithstanding, the impact of pseudo-random modulation on measured conducted electromagnetic interference is still unclear for the low frequency range.For instance, evaluating any electromagnetic interference noise spreading approach is not apparent once it depends on several pseudo-random modulation parameters, dwell time and resolution bandwidth. The conventional super-heterodyne frequency band stepping EMI test receiver defines the dwell time and the resolution bandwidth. For this reason, historically, pseudo-random modulations have been questioned and accused of being a fraudulent way of passing regulations. Nonetheless, research has been presented over the last decades to support the electromagnetic interference noise spreading approach in practical situations, e.g., with power line communication systems.Thus, the research carried out and presented in this thesis, all in all, proposed pseudo-random modulation control algorithms with a random stream based on different methods. The National Instruments PXI with Field-Programmable Gate Array was used as a cyber-physical platform for fast prototyping. This engineering tool enables the necessary electromagnetic interference investigations. The detail Field-Programmable Gate Array implementation and benchmarking of each studied methods are given. Although pseudo-random modulation methods exist previously to this v VI SUMMARY thesis work, the main novelty of the proposed developments lies in the identification and selection of the most suited random stream method for the problem at hand, and its necessary modification for implementation in a fixed-point processor. In this way demonstrating that it is possible to develop new, dedicated converter control algorithms that allow shaping the spectrum of disturbances to meet standardization or technical requirements.Furthermore, once it has been found that the conventional EMC test based on frequency scanning might not be sufficient to evaluate pseudo-random modulated DC/DC converter appropri...

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Section: Conducted Emission Behaviourmentioning

confidence: 77%

Section: Conducted Emission Behaviourmentioning

confidence: 95%

Modulation Algorithms of Power Electronic Converters for Shaping of Conducted Interference

Loschi¹

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Dynamic Analysis of Fuzzy Systems

2023

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In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results.

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Comparison of Neural Network Topologies for Sensor Virtualisation in BEV Thermal Management

Wray,

Pipes,

Dutta

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach. The solution proposed intends to use a combination of the states determined in other sensors and the control inputs made into the system to predict the state of the sensor to be virtualised, with the aim of an average accuracy of 95% and a worst-case accuracy of 80%. Also discussed are the potential methods of nonlinear system identification that can be used to achieve these goals, concluding through a literature review that a Neural Network solution is the most probable method to produce an accurate result. Based on this an analysis is performed of the challenges of neural network development, from collecting and processing data, to actually training the neural network and evaluating the performance outcome. Establishing that depending on the quality and quantity of data collection a range of methods that could be implemented.</div></div>

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Evaluation of Nonlinear ARX System Identification Technique on Modeling Crosstalk

Cited by 3 publications

References 15 publications

Modulation Algorithms of Power Electronic Converters for Shaping of Conducted Interference

Modulation Algorithms of Power Electronic Converters for Shaping of Conducted Interference

Dynamic Analysis of Fuzzy Systems

Comparison of Neural Network Topologies for Sensor Virtualisation in BEV Thermal Management

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