Neural Fuzzy Control of Ball and Beam System

2017 ◽  
Vol 6 (2) ◽  
pp. 64-78
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Fuzzy Logic ◽  
Equations Of Motion ◽  
Membership Functions ◽  
Fuzzy Controllers ◽  
Simulink Model ◽  
Beam System ◽  
Beam Orientation ◽  
Simulation Results ◽  
Neural Fuzzy ◽  
Ball And Beam System

This paper presents an offline control of ball and beam system using fuzzy logic. The objective is to control ball position and beam orientation using fuzzy controllers. A Matlab/Simulink model of the proposed system has been designed using Newton's equations of motion. The fuzzy controllers were built using seven gbell membership functions. The performance of proposed controllers was compared in terms of settling time, steady state error and overshoot. The simulation results are shown with the help of graphs and tables which illustrates the effectiveness and robustness of proposed technique.

A Study Of Different Controller Strategies For A Ball And Beam System

2012 ◽  
Author(s):  
Herman Wahid ◽  
Mohd Fua’ad Rahmat
Keyword(s):  
Fuzzy Logic ◽  
Vertical Plane ◽  
Pole Placement ◽  
Step Response ◽  
Graphic User Interface ◽  
Intelligent Controller ◽  
Beam System ◽  
Controller Performance ◽  
Solid Ball ◽  
Ball And Beam System

Artikel ini membincangkan beberapa pendekatan untuk sistem kawalan yang terdiri daripada pengawal konvensional, pengawal moden dan pengawal pintar, dan akan menguji kecekapan setiap pengawal pada sistem bola dan pengimbang. Sistem bola dan pengimbang adalah satu contoh sistem tak linear serta tidak stabil. Ia terdiri daripada kayu pengimbang yang bebas berputar pada paksi, dengan sebiji bola bergerak di sepanjang batang pengimbang. Objektif sistem kawalan ialah untuk meletakkan bola pada kedudukan tertentu di atas batang pengimbang dengan mengawal voltan motor sebagai input kepada sistem. Artikel ini mengkaji pengawal P dan pengawal PD sebagai kawalan konvensional, perletakan kutub sebagai kawalan moden, dan logik kabur sebagai kawalan pintar. Permodelan matematik untuk sistem bola dan pengimbang dihuraikan yang melibatkan proses penglinearan model agar dapat digunakan dengan pengawal linear. Kemudian, semua pengawal tadi direka bentuk dan disimulasikan dengan menggunakan program MATLAB. Kecekapan setiap pengawal dianalisis berdasarkan beberapa ciri sambutan langkah. Pengantaramuka Pengguna Bergrafik (GUI) yang sesuai telah dibangunkan bagi memberi gambaran animasi untuk sistem bola dan pengimbang. Kata kunci: Sistem bola dan pengimbang; permodelan; pengawal PD; perletakan kutub; logik kabur This paper presents several control approaches that consist of a conventional controller, modern controller and intelligent controller and the performance of those controllers that were employed in a ball and beam system. The ball and beam system is one of the examples of a nonlinear and unstable control system. It consists of rigid beam which is free to rotate in the vertical plane at the pivot, with a solid ball rolling along the beam. The control problem is to position the ball at a desired point on the beam by controlling the motor voltage as the input of the system. Scope of investigation is on proportional (P) and proportional–derivative (PD) as the conventional controller, pole placement for the modern controller, and fuzzy logic for the intelligent controller. The mathematical modeling is linearised in order to be used with the linear control and followed by designing the entire system and simulation in MATLAB. Each controller performance is analyzed and compared using the step response. An appropriate graphic user interface (GUI) has been developed to view the animation of the ball and beam system. Key words: Ball and beam; modeling; PD controller; pole placement; fuzzy logic

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.

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.

Control Optimisation of Overhead Gantry Cranes via Fuzzy Controllers

2021 ◽  
pp. 302-321
Author(s):  
Ashwani Kharola
Keyword(s):  
Gantry Crane ◽  
Membership Functions ◽  
Overhead Crane ◽  
Fuzzy Controllers ◽  
Triangular Shape ◽  
Simulink Model ◽  
Control Optimisation ◽  
Load Swing ◽  
Gantry Cranes ◽  
Varying Mass

This study considers a fuzzy logic-based reasoning approach for control and optimising performance of overhead gantry crane. The objective of this study is to minimise load swing and to stabilise the crane in the least possible time. The fuzzy controllers were designed using nine Gaussian and triangular shape membership functions. The results clearly confirmed the effect of shape of memberships on performance of fuzzy controllers. Performance of overhead crane was measured in terms of settling time and overshoot ranges. The study also demonstrates the influence of varying mass of the load, mass of crane, and length of crane bar on stability of the crane. A mathematical model of the crane system has been derived to develop a simulink model of proposed system and performing simulations.

Integrating Servo-Pneumatic Actuator with Ball Beam System based on Intelligent Position Control

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Muhammad Asyraf Azman ◽  
Ahmad ‘Athif Mohd Faudzi ◽  
Nu’man Din Mustafa ◽  
Khairuddin Osman ◽  
Elango Natarajan
Keyword(s):  
Fuzzy Logic ◽  
Position Control ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Pneumatic Actuator ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Beam System ◽  
Position Controller ◽  
Simulation Results

The purpose of this paper is to design a controller that can control the position of the cylinder pneumatic stroke. This work proposes two control approaches, Proportional-Integral-Derivative Fuzzy Logic (Fuzzy-PID) controller and Proportional-Derivative Fuzzy Logic (PD-Fuzzy) controller for a Servo-Pneumatic Actuator. The design steps of each controller implemented on MATLAB/Simulink are presented. A model based on position system identification is used for the controller design. Then, the simulation results are analyzed and compared to illustrate the performance of the proposed controllers. Finally, the controllers are tested with the real plant in real-time experiment to validate the results obtained by simulation. Results show that PD-Fuzzy controller offer better control compared to Fuzzy-PID. A Pneumatic Actuated Ball & Beam System (PABBS) is proposed as the application of the position controller. The mathematical model of the system is developed and tested simulation using Feedback controller (outer loop)-PD-Fuzzy controller (inner loop). Simulation result is presented to see the effectiveness of the obtained model and controller. Results show that the servo-pneumatic actuator can control the position of the Ball & Beam system using PD-Fuzzy controller.

scholarly journals Modelling and Simulation of DC Motor Speed Control Using Fuzzy-PID Algorithm

2019 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Anrico Gideon Alfano ◽  
Hari Maghfiroh ◽  
Irwan Iftadi ◽  
Chico Hermanu B.A. ◽  
Feri Adriyanto
Keyword(s):  
Fuzzy Logic ◽  
Hybrid Algorithm ◽  
Computation Time ◽  
Dc Motor ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Motor Speed ◽  
Pid Algorithm ◽  
Simulation Results ◽  
The Difference

<p class="Abstract">This paper discusses the simulation of the Fuzzy-PID hybrid algorithm as a method for controlling the speed of a DC motor compared to the usual PID method. Comparisons were also made to the membership functions used in the fuzzy logic fuzzification process. Membership functions that are used are triangular, trapezoidal, and gaussian shaped function with each having 3, 5, and 7 as the number of membership to be compared. The performance of this control is compared by looking at the results of the step unit responses and the ability in tracking signals. The simulation results show that the Fuzzy-PID algorithm is superior to the ordinary PID algorithm but slower on computation time. The results of the comparison in the fuzzification process shows that the more number used in membership function, the faster the response reaches the set point, while the difference in shape has little effect on the response. The best results are achieved by fuzzy triangle shaped function with 7 membership number.</p>

Genetically tuned fuzzy controlled flywheel powered micro-grid for improved frequency control

2020 ◽  
pp. 0309524X2093254
Author(s):  
Saira Manzoor ◽  
Mairaj-ud-Din Mufti
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Frequency Control ◽  
Frequency Deviation ◽  
Electrical Machine ◽  
Membership Functions ◽  
Micro Grid ◽  
Simulation Results ◽  
Operational Constraints ◽  
System Frequency

In this article, hybrid wind-diesel system is powered with a genetically tuned fuzzy controlled flywheel for improving its frequency control. Flywheel is interfaced with the system through an electrical machine (generator/motor) and an electronic converter for synchronization. Fuzzy logic controller for the flywheel is designed in such a way that it continuously controls the system frequency and simultaneously satisfies the operational constraints of flywheel. Fuzzy logic controller is optimized by genetically tuning its membership functions. Regulated variable based on frequency deviation of system and speed characteristics of flywheel is introduced to reach out to the optimized membership functions. Necessary modeling has been done and effectiveness of the assembly has been confirmed by the simulation results.

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