Abstract
This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.
Highlights
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]
The general architecture of adaptive neuro-fuzzy inference system (ANFIS) is ANFIS uses neural networks (NN) to adjust the parameters of membership functions functions (MFs) while tuning fuzzy rules
flyback converter (FC) with unity feedback, FL controller (FLC), ANFIS, and PID controllers were simulated for a fixed load
Summary
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]. 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 been employed to control FCs. FLCs are based on natural language and employ fuzzy “if-” rules and fuzzy reasoning. Line and load regulation and response to reference voltage changes are presented and the static and dynamic performance of the proposed controller is analyzed.
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