# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 1 Introduction  
DC-DC converters are employed in a variety of applications such as industrial controls, audio applications, power adapters and chargers, electric vehicles, electronic appliances, power supplies, renewable systems, aerospace equipment, and many other modern types of equipment that operate on DC [1; 2; 3]. Flyback converters (FCs) are a frequently used type of DC converter which may operate over a broad range of unregulated DC voltage. FCs serve as energy storage as well as converter isolation because of a choke in their topology. This results in reduced noise interference and provided load protection [4; 5]. FCs are able to achieve high efficiency, high stability, small size, lightweight, less cost, etc., and have more power capacity (usually in the range of 20 to 200 W [6]) than other fundamental DC-DC converters. They are preferred for their outstanding features including output voltage waveform shape, power factor control optimization, and device miniaturization [7; 8; 9; 10]. FCs exhibitnon-minimum phase characteristics, i.e., the existence of a right half plane zero (RHPZ) in the voltage transfer function. This complicates the dynamics of FCs, as it results in increased gain and introduces additional phase lag, thus behaving as a pole. Thus, there is a need to design a feedback controller able to ensure the static and dynamic performance of the converter [11]. There are several analog and digital methods, such as PID, fuzzy logic (FL), and sliding mode control (SMC), for regulating the output of DC-DC FCs [12]. The feedback controller in a DC-DC converter is mainly responsible for delivering regulated supply, ideally with no steady-state error, less overshoot, and fast dynamic response while maintaining the highest possible efficiency [13; 14]. Sliding mode control (SMC) based on the equivalent control method with a constant frequency has been applied to FCs in References [5; 14]. In Reference [15], a peak current control technique for a flyback converter's power factor correction was proposed. Reference [16] introduced average current mode control (ACM) for an FC, which employed an outer voltage feedback loop responsible for maintaining constant output voltage, and an inner loop engaged in sensing the input current. Quasi-resonant (QR) controllers for FCs have been presented in References [8; 17; 18]. PI controllers [19] and PID controllers [20; 21] for FCs have also been reported in the literature. For example, in Reference [22], an FC with a photovoltaic (PV) panel was controlled by a PI compensator. The problem with these controllers is that they may not be robust against disturbance and uncertainty. Dynamic modeling and quantitative controller design of a current-mode controlled FC with optocoupler isolation were presented in Reference [23]. In Reference [24], the FC operated with a natural switching surface (NSS) control. Derivation and implementation of NSS with the operation of FCs in boundary conduction mode (BCM) were presented. A detailed review of the controllers to date, including pulse frequency modulation (PFM) control, adaptive peak current value control, current estimation control, and duty cycle control for FCs is presented in Reference [25]. Owing to features such as easier implementation and robustness characteristics, fuzzy logic controllers (FLCs) have also been employed to control FCs. FLCs are based on natural language and employ fuzzy \"if-then\" rules and fuzzy reasoning. They are tolerant of imprecise data, require fewer tuning parameters, and are flexible in such a way that they add more functionality without re-starting from the start [26; 27]. Such an FLC can be employed to implement a central action for dealing with the variable structure nature of an FC. In Reference [28], an FLC-based FC is presented where the FLC responds rapidly to changes in load current or input voltage to ensure better load regulation. The controller also offers robustness with a good dynamic response.