On the stability domain of a DC-DC buck converter with software control loop

Amariutei, Roxana-Daniela; Goraș, Liviu; Dobler, Markus; Rafaila, Monica; Buzo, Andi; Pelz, Georg

doi:10.1109/icstcc.2015.7321394

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Due to the progress of computational power of computer systems this is no longer a limiting factor. Over a period of time, the D-decomposition technique has been modified and improved [34][35][36][37][38]. For instance, it can take into account constraints such as gain and phase margins [39], yet works showing applications of the D-decomposition technique to the mechatronic system are rare in the literature.…”

Section: Introductionmentioning

confidence: 99%

Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System

et al. 2020

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In this work, issues related to the application of the D-decomposition technique to selection of the controller parameters for a drive system with flexibility are presented. In the introduction the commonly used control structures dedicated to two-mass drive systems are described. Then the mathematical model as well as control structure are introduced. The considered structure has only basic feedbacks from the motor speed and PI type controller. Due to the order of the closed-loop system, the free location of the system’s poles is not possible. Large oscillations can be expected in responses of the plant. In order to improve the characteristics of the drive, the tuning methodology based on the D-decomposition technique is proposed. The initial working point is selected using an analytical formula. Then the value of controller proportional gain is decreasing, until the required value of overshoot is obtained. In the paper different advantages of the D-decomposition technique are presented, for instance calculation of global stability area for the selected gain and phase margin, the impact of parameter changes, and additional delay evident in the system. Theoretical considerations are confirmed by simulation and experimental results.

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Section: Introductionmentioning

confidence: 99%

Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System

et al. 2020

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“…Several studies have been conducted to estimate the circuit model in electronic testing research using black-box modeling through a system identification (SI) technique and artificial intelligence (AI) [4]- [8]. However, these studies have not focused on FRD for a power management circuit, such as the failure region of an LVR [9]- [14].…”

Section: Introductionmentioning

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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“…Much research have been done to test and analyse the analog circuit without knowing the exact model of the circuit under test, such as through transfer coefficient based [14]- [16] and black-box modelling [17]. Some work also have been conducted to estimate the failure region boundary of analog circuit [18]- [20]. In addition, most research on capacitor ESR only concentrates for the purpose of condition monitoring and fault diagnosis [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

Zaman

Mustafa

Hussain

2017

International Journal on Advanced Science, Engineering and Information Technology

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Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%.

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On the stability domain of a DC-DC buck converter with software control loop

Cited by 5 publications

References 13 publications

Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System

Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System

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

Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

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