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

2016 ◽  
Vol 17 (05) ◽  
pp. 1740007 ◽  
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.

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.

scholarly journals ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

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.

scholarly journals An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
M. Ajay Kumar ◽  
N. Srikanth
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Pulse Width ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Light Transmission ◽  
Inference System ◽  
Hvdc Light ◽  
Neuro Fuzzy

AbstractIn HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

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

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.

scholarly journals Robot navigation with obstacle avoidance in unknown environment

2018 ◽  
Vol 7 (4) ◽  
pp. 2410 ◽  
Author(s):  
Neerendra Kumar ◽  
Zoltán Vámossy
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Fuzzy Inference System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Training Data ◽  
Inference System ◽  
Pure Pursuit

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

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 Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
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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