PSO Trained ANFIS MPPT for Solar PV systemIn this Work, an ANFIS technique using experimental data is designed for predicting the maximum power point of a ph...

### 초록 본 연구는 산업용 로봇 기반 용접 공정에서 용접 품질을 실시간으로 예측하고 제어하기 위해 적응형 신경 퍼지 추론 시스템(ANFIS) 기반의 고급 제어 알고리즘을 제안한다. 용접 공정은 다양한 요소에 의해 품질 변동성이 크게 발생할 수 있으며, 숙련된 작업자의 경험에 의존하는 경우가 많다. 본 연구에서는 용접 전류, 전압, 속도, 용접봉 각도 등 다양한 공정 변수를 실시간으로 수집하고, 수집된 데이터를 기반으로 ANFIS 모델을 학습시켜 용접 품질을 예측한다.

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PSO Trained ANFIS MPPT for Solar PV systemIn this Work, an ANFIS technique using experimental data is designed for predicting the maximum power point of a ph...

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this work, an ANFIS-MPPT method based on a large simulation training data is designed to avoid the system from experiencing a high training error. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

An adaptive network-based fuzzy inference system (ANFIS) ...