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

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.

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i5.pp3657-3665 ◽

2018 ◽

Vol 8 (5) ◽

pp. 3657 ◽

Cited By ~ 1

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.

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Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

International Journal of Engineering & Technology ◽

10.14419/ijet.v6i2.7722 ◽

2017 ◽

Vol 6 (2) ◽

pp. 45 ◽

Cited By ~ 1

Author(s):

Ravi Kumar Sharma ◽

Dr. Parul Gandhi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Software Systems ◽

Data Sets ◽

Component Reliability ◽

Inference System ◽

Neuro Fuzzy ◽

Proposed Model ◽

Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

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ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

Open Engineering ◽

10.1515/eng-2016-0075 ◽

2016 ◽

Vol 6 (1) ◽

Author(s):

Manuel Braz César ◽

Rui Carneiro Barros

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Degrees Of Freedom ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Magnetorheological Dampers ◽

Inference System ◽

Neuro Fuzzy ◽

Three Degrees Of Freedom

Abstract In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

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Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure

International Journal of Structural Stability and Dynamics ◽

10.1142/s0219455417400077 ◽

2016 ◽

Vol 17 (05) ◽

pp. 1740007 ◽

Cited By ~ 3

Author(s):

Manuel Braz-César ◽

Rui Barros

Keyword(s):

Fuzzy Logic ◽

Structural Control ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Inference System ◽

Mr Dampers ◽

Neuro Fuzzy ◽

Adaptive Control Systems ◽

Robust Systems

Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.

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An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽

10.3390/act10030051 ◽

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.

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A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System

Composite Structures ◽

10.1016/j.compstruct.2017.03.028 ◽

2017 ◽

Vol 170 ◽

pp. 215-227 ◽

Cited By ~ 17

Author(s):

H. Naderpour ◽

S.A. Alavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Rc Beams ◽

Inference System ◽

Neuro Fuzzy ◽

Proposed Model

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Adaptive neuro-fuzzy controller of switched reluctance motor

Serbian Journal of Electrical Engineering ◽

10.2298/sjee0701023t ◽

2007 ◽

Vol 4 (1) ◽

pp. 23-34 ◽

Cited By ~ 11

Author(s):

Ahmed Tahour ◽

Hamza Abid ◽

Ghani Aissaoui

Keyword(s):

Fuzzy Inference ◽

Fuzzy Controller ◽

Expert Knowledge ◽

Digital Simulation ◽

Switched Reluctance Motor ◽

Motor Speed ◽

Inference System ◽

Switched Reluctance ◽

Neuro Fuzzy ◽

Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

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Position and Tilt Control of Two-Wheeled Robot (TWR)

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2017100102 ◽

2017 ◽

Vol 6 (4) ◽

pp. 17-33 ◽

Cited By ~ 1

Author(s):

Ashwani Kharola ◽

Pravin P. Patil

Keyword(s):

Fuzzy Inference ◽

Second Law ◽

Membership Functions ◽

Inference System ◽

Control Approach ◽

Simulink Model ◽

Neuro Fuzzy ◽

Wheeled Robot ◽

Simulation Results ◽

Training Error

This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot (TWR) system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference system(ANFIS) controller. A Matlab Simulink model of the system has been built using Newton's second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.

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