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

Electronic systems are becoming increasingly so-phisticated 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 elec-tronic systems nowadays are in the form of system-on-chip and thus require stable voltage specifications. One of the critical elec-tronic 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.

@article{Luet2020,
  title     = {Physical Parameter Estimation of Linear Voltage Regulators using Model-based Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {10},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Ng Len Luet and Mohd Hairi Mohd Zaman and Asraf Mohamed Moubark and M Marzuki Mustafa},
  doi       = {10.14569/IJACSA.2020.0111072},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111072}
}