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.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and 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.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle control of Inverted Pendulum (IP) Systems

2016 ◽  
Vol 5 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Ashwani Kharola
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Comparison Study ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

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 An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

scholarly journals T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Boutaina Elkinany ◽  
Mohammed Alfidi ◽  
Redouane Chaibi ◽  
Zakaria Chalh
Keyword(s):  
State Feedback ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Pendulum System ◽  
Great Ability ◽  
Control Approach ◽  
Feedback Model ◽  
Inverted Pendulum System ◽  
System Controller ◽  
Fuzzy State

This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H ∞ , given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.

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