Integrated Power Signature Generation Circuit for IoT Abnormality Detection

This work presents a methodology to monitor the power signature of IoT devices for detecting operation abnormality. It does not require bulky measurement equipment thanks to the proposed power signature generation circuit which can be integrated into LDO voltage regulators. The proposed circuit is implemented using a 130 nm CMOS technology and simulated with power trace measured from a wireless sensor. It shows the generated power signature accurately reflects the power consumption and can be used to distinguish different operation conditions, such as wireless transmission levels, data sampling rates and microcontroller UART communications.

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.