Control Dynamics and Simulation of Inclined Cart and Pendulum System

2017 ◽  
Vol 8 (2) ◽  
pp. 73-89
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Fuzzy Inference ◽  
Control Strategies ◽  
Inference System ◽  
Pendulum System ◽  
Simulink Model ◽  
System A ◽  
Neuro Fuzzy ◽  
Mathematical Equations ◽  
Overshoot And Undershoot ◽  
Inclined Surface

This paper applies various soft-computing control strategies for offline mode control of highly non-linear cart and pendulum system moving on an inclined surface. The surface is considered at inclination of 12° from horizontal. The study compares performance of four different control techniques namely Proportional-integral-derivative (PID), Fuzzy logic, Adaptive neuro fuzzy inference system (ANFIS) and Neural networks for control of proposed system. A Matlab-Simulink model of system has been developed from mathematical equations derived using Newton's second law. The cart and pendulum system has been initially controlled using PID controllers and results were further used to train ANFIS and neural controllers. The ANFIS and fuzzy controllers were designed using three and nine gbell shape membership functions (MFs) respectively. The controllers were further compared in terms of settling time, overshoot and undershoot.

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

2017 ◽  
Vol 6 (4) ◽  
pp. 17-33 ◽  
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.

scholarly journals ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

2015 ◽  
Vol 123 (13) ◽  
pp. 32-38 ◽  
Author(s):  
Navneet Walia ◽  
Harsukhpreet Singh ◽  
Anurag Sharma
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy

Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control

2019 ◽  
Vol 44 (2) ◽  
pp. 125-141
Author(s):  
Satyabrata Sahoo ◽  
Bidyadhar Subudhi ◽  
Gayadhar Panda
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Inference System ◽  
Proportional Integral ◽  
Simulink Model ◽  
Neuro Fuzzy

This article presents a multiple adaptive neuro-fuzzy inference system-based control scheme for operation of the wind energy conversion system above the rated wind speed. By controlling the pitch angle and generator torque concurrently, the generator power and speed fluctuation can be reduced and also turbine blade stress can be minimized. The proposed neuro-fuzzy-based adaptive controller is composed of both the Takagi–Sugeno fuzzy inference system and neural network. First, a step change in wind speed and then a simulated wind speed are considered in the proposed adaptive control design. A MATLAB/Simulink model of the wind turbine system is prepared, and simulations are carried out by applying the proportional integral, fuzzy-proportional integral and the proposed adaptive controller. From the obtained results, the effectiveness of the proposed adaptive controller approach is confirmed.

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

2018 ◽  
pp. 863-880
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.

scholarly journals Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective

2020 ◽  
Vol Volume 13 ◽  
pp. 355-371
Author(s):  
Jasleen Kaur ◽  
Asif Irshad Khan ◽  
Yoosef B Abushark ◽  
Md Mottahir Alam ◽  
Suhel Ahmad Khan ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Web Application ◽  
Fuzzy Inference ◽  
Security Risk ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy ◽  
Design Perspective ◽  
Security Risk Assessment
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