I. Introduction
In recent years, resonant converters have gained popularity in many applications such as power factor corrector systems [1], bidirectional converters as the interface between the battery packs and renewable sources in hybrid automobiles and photovoltaic systems [2] –[4], microrobot drivers [5] , LED drivers [6]–[9] , and battery chargers [10], [11] . Unlike pulse width modulation (PWM) converters, resonant converters transfer power through a high-frequency resonant tank making the dynamic and steady state much more intricate than the PWM converter counterpart. Such increased complexity makes design and control of resonant converters more challenging. The traditional strategy to control power converters involves small-signal modeling by performing perturbation and linearization. Since this technique only considers the converter operation around the equilibrium operating point, it cannot provide sufficient information related to the large-signal behavior of the system, resulting in a poor response outside the designed operating point [12]. In spite of such difficulties, small-signal and frequency analysis have been popular tools in modeling, design, and implementation due to their simplicity [13]. Following with the small-signal analysis trend, simplified models of series and parallel resonant converters have been presented based on first harmonic approximation, including transfer functions for both frequency and duty cycle [14]. Another technique that has been used are sampled data models that are employed to reduce the transient and steady-state errors by dynamically controlling the switching frequency [15]. By performing perturbation methods [16], a small-signal model is suggested for the series resonant converter (SRC). Also, a small-signal discrete time model using the microsynthesis technique is introduced in [17]. However, while all these techniques can describe the small-signal behavior of the SRC successfully, they are not able to provide sufficient information about the actual large-signal dynamic nature of the system.