Adaptive Neuro-fuzzy Inference System Design of Inverted Pendulum System on an Inclined Rail

2010 ◽  
Author(s):  
Xianran Jia ◽  
Yaping Dai ◽  
Zubair Ahmed Memon
Keyword(s):  
System Design ◽  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Neuro Fuzzy

scholarly journals Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

2018 ◽  
Vol 8 (5) ◽  
pp. 3657 ◽  
Author(s):  
Mohammed A. A. Al-Mekhlafi ◽  
Herman Wahid ◽  
Azian Abd Aziz
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Inference System ◽  
Pendulum System ◽  
Control Approach ◽  
Inverted Pendulum System ◽  
Neuro Fuzzy

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.

Stabilization and Control of Elastic Inverted Pendulum System (EIPS) Using Adaptive Fuzzy Inference Controllers

2017 ◽  
Vol 6 (4) ◽  
pp. 21-32
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Transverse Displacement ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Highly Nonlinear ◽  
Training Error ◽  
And Control

Elastic Inverted Pendulum system (EIP) are very popular objects of theoretical investigation and experimentation in field of control engineering. The system becomes highly nonlinear and complex due to transverse displacement of elastic pole or pendulum. This paper presents a comparison study for control of EIP using fuzzy and hybrid adaptive neuro fuzzy inference system (ANFIS) controllers. Initially a fuzzy controller was designed, which was used for training and tuning of ANFIS controller using gbell shape membership functions (MFs). The performance of complete system was evaluated through output responses of settling time, steady state error and maximum overshoot. The study also highlights effect of varying number of MFs on training error of ANFIS. The results showed better performance of ANFIS controller compared to fuzzy controller.

Inverted Pendulum System Modeling and Fuzzy Neural Networks Control

2012 ◽  
Vol 268-270 ◽  
pp. 1371-1375
Author(s):  
Hao Yu
Keyword(s):  
Neural Network ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Fuzzy Neural Networks ◽  
Model Parameters ◽  
Adaptive Neural Network ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System

Inverted pendulum on a cart poses a challenging control problem. It seems to have been one of attractive tools for testing linear and nonlinear control laws. In this paper, we adopt PID and the adaptive neural network based fuzzy inference method to control the inverted pendulum, combined the fuzzy control into the neural control. This method can improve the capability of the fuzzy controller through learning the data of PID controller to train the fuzzy controller. When the model parameters were changed, the adaptive neural network based fuzzy inference system had good adopt ability to anti-interfere. The cart can go to the destine position exactly.

scholarly journals Intelligent Control for Self-erecting Inverted Pendulum Via Adaptive Neuro-fuzzy Inference System

2006 ◽  
Vol 3 (4) ◽  
pp. 1795-1802 ◽  
Author(s):  
A.A. Saifizul ◽  
Z. Zainon ◽  
N.A Abu Osman ◽  
C.A. Azlan ◽  
U.F.S Ungku Ibrahim
Keyword(s):  
Intelligent Control ◽  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

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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