An ANFIS-PI based boost converter control scheme

The PI algorithm has proven to be a popular and widely used control method, due to its relative simplicity and robustness. Despite this, the linear nature of the algorithm means it doesn't provide optimal control to non-linear systems. This paper presents a novel method of improving the performance of the PI controller using an ANFIS network to provide gain scheduling. This control scheme is applied to a Boost Converter circuit and simulated within the PSIM modelling environment. The simulation results indicate that using the ANFIS controller provides a fast system response with minimal errors even under dynamic operating conditions. The ANFIS controller is also shown to simplify the design flow in comparison to the popular Fuzzy-PI gain scheduling method

A Novel ANFIS Algorithm Architecture for FPGA Implementation

This paper presents a new architecture for the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm targeting FPGA implementation. This new architecture offers higher efficiency and scalability in comparison to the existing methods. The proposed architecture is modeled and simulated using VHDL and is targeted at a Xilinx FPGA. Existing implementation architectures are also modeled and comparisons are drawn between them in terms of both performance and logic utilization. The results show that the new architecture offers a reduction in calculation cycles of around 50% in comparison to the architecture from which it’s derived. This increase in calculation speed comes with only a modest increase in logic utilization, specifically a 2.5% increase in look-up table (LUT) usage and a 1.5% increase in flip-flop usage. The new architecture also eliminates scalability issues which can arise in the existing architectures when extra input members are required