Black-box modeling of low dropout voltage regulator based on two-port network parameter identification

Zaman, ‪Mohd Hairi Mohd; Mustafa, Mohd. Marzuki; Hussain, Aini

doi:10.1109/cspa.2015.7225622

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…An MBA is a method for describing system behavior in the forms of transfer functions by analyzing the circuit. The transfer function of a system provides useful information regarding the input and output, such as stability, steady-state response, and dynamic response [25], [26]. In this study, the MBA is aimed at identifying all the coefficients or unknown parameters presented in a transfer function through the analysis of input and output signals.…”

Section: A Model-based Approachmentioning

confidence: 99%

Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach

Luet¹,

Hairi²,

Mohamed³

et al. 2020

IJACSA

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Electronic systems are becoming increasingly sophisticated due to the emergence of advanced technology, which can produce robust integrated circuits by reducing the dimensions of transistors to just a few nanometers. Furthermore, most electronic systems nowadays are in the form of system-on-chip and thus require stable voltage specifications. One of the critical electronic components is the linear voltage regulator (LVR). LVRs are types of power converter used to maintain a stable and constant DC voltage to the load. Therefore, LVR stability is an essential aspect of voltage regulator design. The main factor influencing the stability of LVRs is the load disturbance. In general, disturbances such as a sudden change in load current can be compensated for by an output capacitor, which, contains a parasitic element known as equivalent series resistance (ESR). Therefore, the ESR and output capacitor specified in the datasheet is essential to compensate for load disturbance. However, LVR manufacturers typically do not provide detailed information, such as the internal physical parameters associated with the LVR in the datasheet. This situation leads to difficulties in identifying the behavior and stability of LVR. Therefore, this study aims to develop a method for estimating the internal physical parameters of LVR circuits that are difficult to measure directly by using a model-based approach (MBA). In this study, the MBA estimates the LVR model transfer function by analyzing the input and output signals via a linear regression method. Simulations through MATLAB and OrCAD Capture CIS software verify the estimated LVR model transfer function. Results show that the MBA has an excellent performance in estimating the physical parameters of LVRs and determining their stability.

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Section: A Model-based Approachmentioning

confidence: 99%

Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach

Luet¹,

Hairi²,

Mohamed³

et al. 2020

IJACSA

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show abstract

“…The second test circuit was tested, wherein the LVR circuit was constructed using a commercial LVR of model LT1963A from Linear Technology [25]. This circuit was simulated in the LTSpice software and subsequently constructed in the hardware circuitry form.…”

Section: Lvr Circuit Constructed Using Commercial Lvrmentioning

confidence: 99%

Noninvasive Stability Measurement of Linear Voltage Regulator in the Closed-loop Condition

Zamri¹

2019

IJATCSE

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Few factors may contribute to the stability of a linear voltage regulator (LVR). Among them is the value of the capacitor connected to the output terminal of the LVR, with the primary factor being the parasitic element of the capacitor, namely, equivalent series resistance. The LVR operation is based on the closed-loop system mechanism. LVR stability can be analyzed through two types of responses, namely, transient and frequency. This research focuses on the frequency response of the closed-loop system in terms of the phase margin at the unity gain frequency. Frequency response generates a more accurate outcome than transient response.The transient response can be acquired while the system is in the closed-loop condition, whereas frequency response can only be obtained in the open-loop condition. In this study, a noninvasive method is applied to obtain the LVR frequency response while the system is in the closed-loop condition. LTSpice software is utilized to simulate the LVR circuit. The simulation result is used as a reference for the actual hardware circuitry tests. The obtained outcome shows that the phase margin from the circuit simulation is similar to that of the hardware circuitry. Therefore, the noninvasive stability measurement method is valid for analyzing LVR stability by measuring the phase margin even in the closed-loop condition.

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“…Virtual sensing, using either DDA or MBA, is widely used in various applications. Virtual sensing that uses DDA has been adopted for applications that require big data processing, such as in wireless communication, remote sensing, and noise control [25]- [27]. This situation occurs because performing system modeling is intricate in these areas.…”

Section: Virtual Sensingmentioning

confidence: 99%

Failure Region Determination of Linear Voltage Regulator Using Data-driven and Model-based Virtual Sensing

Zaman¹

2019

IJATCSE

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Disturbance to the output of a linear voltage regulator (LVR) due to an abrupt change in output current can be compensated by using an output capacitor. A capacitor has an internal parasitic resistive element known as equivalent series resistance (ESR). However, the ESR value may change due to aging and temperature variations, forming a failure region in an LVR. Besides, the performance of each manufactured LVR varies due to differences in the manufacturing process. Consequently, failure region determination (FRD) for LVRs involves time-consuming and costly manual data acquisition and requires analysis to determine the failure region accurately. In this work, efficient and effective FRD methods were developed by applying the concept of virtual sensing. Two approaches were used, namely, a data-driven approach (DDA) and a model-based approach (MBA). The developed FRD methods were as follows: the data interpolation method (DDA-DIM), the input-output model-based method (MBA-IOM), and the circuit analysis model-based method (MBA-CAMM). DDA-DIM utilizes a multilayer perceptron and a radial basis function neural network. Meanwhile, MBA-IOM and MBA-CAMM estimate the black-box model of an LVR and the circuit analysis model, respectively. The results of the three methods were compared with the benchmark developed using manual FRD. MBA-CAMM was determined as the most effective and efficient FRD method that applies the virtual sensing concept.

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Black-box modeling of low dropout voltage regulator based on two-port network parameter identification

Cited by 5 publications

References 15 publications

Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach

Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach

Noninvasive Stability Measurement of Linear Voltage Regulator in the Closed-loop Condition

Failure Region Determination of Linear Voltage Regulator Using Data-driven and Model-based Virtual Sensing

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