This paper presents a light-emitting diode (LED) driver for street-lighting applications that uses a resonant rectifier as a power-factor corrector (PFC). The PFC semi-stage is based on a zero-current and zero-derivative-switching (ZCDS) Class-E current-driven rectifier, and the LED driver semi-stage is based on a zero-voltage-switching (ZVS) Class-D LLC resonant converter that is integrated into a single-stage topology. To increase the conduction angle of the bridge-rectifier diodes current and to decrease the current harmonics that are injected in the utility line, the ZCDS Class-E rectifier is placed between the bridge-rectifier and a DC-link capacitor. The ZCDS Class-E rectifier is driven by a high-frequency current source, which is obtained from a square-wave output voltage of the ZVS Class-D LLC resonant converter using a matching network. Additionally, the proposed converter has a soft-switching characteristic that reduces switching losses and switching noise. A prototype for a 150-W LED street light has been developed and tested to evaluate the performance of the proposed approach. The proposed LED driver had a high efficiency (>91%), a high power-factor (>0.99), and a low THDi (<8%) under variation of the utility-line input voltage from 180~250 Vrms. These experimental results demonstrate the feasibility of the proposed LED scheme.

This study presents a cost-efficient Hardware-in-the-Loop platform for liquid-level process control education, designed to bridge the gap between theoretical learning and real-world industrial practice. The proposed system integrates NI myRIO and NI myDAQ hardware with LabVIEW-based real-time simulation and controller implementation, enabling flexible experimentation across a range of linear and nonlinear tank models. Through real-time controllers, students can design, tune, and validate classical digital controllers while gaining hands-on experience with real-time process dynamics. Experimental results from Model-in-the-Loop and Hardware-in-the-Loop configurations confirm the high accuracy between simulated and hardware responses, with low normalized root mean square error (NRMSE < 0.07) and high normalized cross-correlation (NCC > 0.99) between MIL and HIL responses. Additionally, learning outcomes were assessed using rubrics and student perception surveys aligned with ABET criteria. The platform successfully satisfies ABET student outcomes (SO1, SO2, SO7) by promoting modeling, system identification, and real-time implementation skills. Student surveys reveal high satisfaction mean = 5.44 and a Cronbach’s α of 0.91367, highlighting enhanced engagement, flexibility, and confidence in control system design. This work demonstrates an adaptable, scalable educational solution that strengthens engineering competencies while keeping implementation costs low.