Intelligent control of nonlinear inverted pendulum system using Mamdani and TSK fuzzy inference systems: a performance analysis without and with disturbance input

2018 ◽  
Vol 2 (3/4) ◽  
pp. 313 ◽  
Author(s):  
Lal Bahadur Prasad ◽  
Hari Om Gupta ◽  
Barjeev Tyagi
Keyword(s):  
Performance Analysis ◽  
Intelligent Control ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Inference Systems ◽  
A Performance

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.

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