Adaptive Neuro-Fuzzy Inference System-Based Grey Time-Varying Sliding Mode Control for Power Conditioning Applications

2015 ◽  
Author(s):  
En-Chih Chang ◽  
Rong-Ching Wu ◽  
Ke Zhu ◽  
Guan-Yu Chen
Keyword(s):  
Sliding Mode Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Time Varying ◽  
Power Conditioning ◽  
Inference System ◽  
Mode Control ◽  
Neuro Fuzzy

scholarly journals ANFIS-sliding mode control of a DFIG supplied by a two-level SVPWM technique for wind energy conversion system

2020 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Habib Benbouhenni
Keyword(s):  
Sliding Mode Control ◽  
Pulse Width Modulation ◽  
Reactive Power ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Inference System ◽  
Wind Energy Conversion ◽  
Mode Control ◽  
Neuro Fuzzy ◽  
Doubly Fed

A modified adaptive neuro-fuzzy inference system sliding mode control (ANFIS-SMC) by using two-level space vector pulse width modulation (SVPWM) for doubly fed induction generator (DFIG) is proposed in this article. ANFIS-SMC with SVPWM strategy improves the basic SMC performances, which features low stator active and reactive power and also minimize the total distortion harmonic (THD) of stator current. The computer simulation results, in Matlab, demonstrate the effectiveness of the proposed control strategy which improves the performance of the DFIG.

scholarly journals AN Adaptive Neuro Fuzzy Inference System Control For A Bearingless Induction Motor Based On A Disturbance Observer

2019 ◽  
Vol 9 (1) ◽  
pp. 906-912
Keyword(s):  
Induction Motor ◽  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Switching Function ◽  
System Control ◽  
Inference System ◽  
Regular Control ◽  
Neuro Fuzzy

There is some poor performance regarding controlling capacity of the bearing-less induction motor (BIM) when there are deviations in the parameters, outer disturbances and changes in the loads. So to solve this issue design of an adaptive exponential sliding-mode (AESM) controller and an observer for extended SM disturbance for finding system disturbance variables while operating are done. This adaptive exponential control is explained by combining order one norm and switching function law into regular control strategy. We can adjust the conjuction speed time adaptively as per variation of the SM switch surface and the system status. The controller used in this control strategy is Adaptive Neuro-Fuzzy Inference System (ANFIS). The observer used senses the speed and outer disturbances of the bearing-less induction motor. As feed forward contribution for system speed, the response of DSMO is utilized. The disturbance in the motor can be reduced by adjusting error in the speed by this feedback speed. From simulation output it can be seen that proposed system with ANFIS control strategy has good strength to control disturbances and to find the uncertain disturbances accurately. Hence the controlling capacity of the bearing-less induction motor (BIM) when there are deviations can be improved by using this proposed system.

Ride comfort analysis of passenger body biodynamics in active quarter car model using adaptive neuro-fuzzy inference system based super twisting sliding mode control

2019 ◽  
Vol 25 (12) ◽  
pp. 1866-1882 ◽  
Author(s):  
Devdutt Singh
Keyword(s):  
Sliding Mode Control ◽  
Human Body ◽  
Ride Comfort ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Inference System ◽  
Quarter Car Model ◽  
Car Model ◽  
Mode Control ◽  
Quarter Car

In this paper, a four degrees of freedom biodynamic human body model is used for ride comfort analysis, which is coupled with a three degrees of freedom quarter car model. The random road profile is generated in a simulation environment using the ISO 8608:2016 standard. In order to suppress the adverse effects of road induced vibrations on the human body, a super-twisting sliding mode control (STSMC) and adaptive neuro-fuzzy inference system (ANFIS) based super-twisting sliding mode control (ASTSMC) strategy is used in the main suspension of the active quarter car model. The ride comfort response of the human body segments is compared for passive and active suspension systems using the ISO 2631-1:1997 standard. Based on the simulation results in time and frequency domain related to acceleration and displacement response for head and neck, upper torso, viscera and lower torso, it is shown that the ride comfort provided by the ASTSMC controller is much improved compared to the STSMC and passive control method. It can be finalized from the present research work that active suspension with the ASTSMC control strategy can successfully reduce the adverse effects of road induced vibrations on human body health and safety.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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