Automated System Identification of Digitally-Controlled Multi-phase DC-DC Converters

Kong, Na; Davoudi, Ali; Hagen, Mark; Oettinger, Eric; Xu, Ming; Ha, Dong Sam; Lee, Fred C.

doi:10.1109/apec.2009.4802665

Cited by 18 publications

(8 citation statements)

References 13 publications

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“…The approaches of system identification to different types of systems are summarized in [6]. In [3], [4], [7] and [9], researchers apply system identification methods into industrial systems such as combustion en gines, DC-DC converters, power systems and medical systems. Since there are factors of saturation and time delay, RF power generators are nonlinear systems.…”

Section: System Identification By Adaptive Pa Ra Meter Estimationmentioning

confidence: 99%

Developing a linear model of RF power generators with pseudo random binary signals (PRBS)

Fang

Dianat

Mestha

et al. 2014

2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)

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In this paper, we will present an approach developing a linear model of a radio frequency (RF) power generator by using pseudo random binary signals (PRBS). We will compare two linear models obtained respectively by the PRBS approach and a traditional modeling approach. The result shows that both approaches achieve a very similar model of the RF power generator. Moreover, it can be shown that the PRBS approach is easily implemented in FPGA (Field Programmable Gate Array) and can be adapted for the on-line system identification.

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Section: System Identification By Adaptive Pa Ra Meter Estimationmentioning

confidence: 99%

Developing a linear model of RF power generators with pseudo random binary signals (PRBS)

Fang

Dianat

Mestha

et al. 2014

2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)

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“…The spectrum analyzer uses FFT method for the frequency response. For digital implementation of frequency response using adaptive filter, the sinusoidal periodic excitation signal is generated using equation (5). The sampling frequency is 10 kHz while the adaptation gain μ is set to 10.…”

Section: Filter Modelmentioning

confidence: 99%

“…In most of the reported power electronic applications, frequency response is analyzed by fast Fourier transform (FFT) or discrete Fourier transform (DFT) [4], [5], [6]. The Fourier transform method uses a linear combination of dilated trigonometric signals to represent the original data.…”

Section: Introductionmentioning

confidence: 99%

Implementation and evaluation of online system identification of electromechanical systems using adaptive filters

Kshirsagar¹,

Jiang²,

Zhang

2014

2014 IEEE Energy Conversion Congress and Exposition (ECCE)

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Online system identification method provides useful insight into the electromechanical plant behavior during commissioning phase as well as health monitoring. In addition to controller tuning, the methods can be applied for identifying un-modelled mechanical resonances, sensor vibrations, bearing friction, etc. Accordingly, this paper focuses on the implementation and evaluation of real time frequency response of electromechanical systems using adaptive filters. Some of the challenges during implementation include selecting the structure of adaptive filters and understanding influence of convergence gain on accuracy of frequency response. In this work, online system identification is demonstrated on two setups (a) magnetic suspension system and (b) sensorless permanent magnet motor drive. The experimental result validate that the method elaborated is simple, easy to implement and accurate when compared with commercial off the shelf spectrum analyzer.

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“…Real-time system identification [25], [26], is used here due to its immunity to parameter uncertainty. The authors have set forth a system identification technique that enables the real-time parameter estimation in situ (in-place), without requiring expensive external test equipments such as network analyzer [26]. Therefore, previous knowledge of the system parameter is not required.…”

Section: Auto-tuning Frameworkmentioning

confidence: 99%

A General Framework for Automated Tuning of Digital Controllers in Multi-phase Dc-Dc Converters

Davoudi

Kong

Hagen

et al. 2009

2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition

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Digital controllers are widely used in interleaved multi-phase dc-dc converters. The inherent nonlinearities introduced by the nonlinear control rules, quantizers, and mapping between analog and digital domains complicates the controller design process. A simple, yet accurate auto-tuning framework is set forth that provides the required controller coefficients to meet desired time and frequency performance criteria. This is an optimization-based process which minimizes a cost function formulated using error terms. The proposed methodology is implemented on a dual-phase buck converter with a regulated output voltage and drastic change in the load current. The desired performance is verified in time and frequency domains using hardware measurements and numerical simulations.

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Automated System Identification of Digitally-Controlled Multi-phase DC-DC Converters

Cited by 18 publications

References 13 publications

Developing a linear model of RF power generators with pseudo random binary signals (PRBS)

Developing a linear model of RF power generators with pseudo random binary signals (PRBS)

Implementation and evaluation of online system identification of electromechanical systems using adaptive filters

A General Framework for Automated Tuning of Digital Controllers in Multi-phase Dc-Dc Converters

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