Intelligent Robust Control Design with Closed-Loop Voltage Sensing for UPS Inverters in IoT Devices

Area of Science:

• Electrical Engineering
• Control Systems
• Artificial Intelligence

Background:

• Uninterruptible Power Supply (UPS) inverters are crucial for protecting Internet of Things (IoT) devices and their data from power disruptions.
• Existing control techniques for UPS inverters can suffer from prediction errors, leading to high harmonic distortion and degraded dynamic response, especially under challenging load conditions.

Purpose of the Study:

• To propose an intelligent and robust control technique for UPS inverters in IoT applications.
• To enhance the reliability and performance of UPS inverters by mitigating prediction errors and improving dynamic response.

Main Methods:

• A modified gray fast variable structure sliding mode control (MGFVSSMC) is synthesized with a neural network (NN).
• The MGFVSSMC ensures rapid system state convergence and eliminates chattering and steady-state errors.
• The NN compensates for MGFVSSMC prediction errors caused by nonlinear loads or parameter variations.

Main Results:

• The combined MGFVSSMC and NN control technique achieves fast transient response and low output voltage distortion.
• The control strategy effectively compensates for prediction errors, maintaining high performance under diverse load conditions.
• Simulations and digital signal processing (DSP) implementations validate the technique's effectiveness.

Conclusions:

• The proposed intelligent control technique significantly improves UPS inverter performance for IoT devices.
• The method offers robust protection against power outages and data loss.
• The integration of MGFVSSMC with NN provides a reliable solution for high-performance UPS inverters.