Fuzzy Logic Control for an Integrated System of a Micro-Manipulator with a Single Flexible Beam

2004 ◽  
Vol 10 (5) ◽  
pp. 755-776 ◽  
Author(s):  
N. G. Chalhoub ◽  
B. A. Bazzi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Angular Displacement ◽  
Digital Simulation ◽  
Linear Quadratic Regulator ◽  
Integrated System ◽  
Flexible Beam ◽  
Linear Quadratic ◽  
End Effector ◽  
Micro Manipulator

The use of lightweight robotic manipulators in advanced assembly and manufacturing applications is hindered by the end-effector positional inaccuracies induced by the structural deformations of the arm. To address this problem, a macro- and micro-manipulator system is considered herein. Three rigid and flexible motion controllers, consisting of an integral plus state feedback controller (ISFC), linear quadratic regulator with an integral action (LQI) and a fuzzy logic controller (FLC), have been implemented in this study. The performances of these controllers are compared based on achieving zero steady-state error in the rigid body angular displacement of the beam, damping out the unwanted vibrations, rendering the end-effector insensitive to the vibrations of the arm, and avoiding excessive control torque requirements. The digital simulation results demonstrate the superiority of the FLC over the ISFC and LQI in damping out the vibrations of the beam and reducing the gripper positional inaccuracies while requiring relatively smaller control torques. Furthermore, the results clearly demonstrate the robustness of the FLC to significant variations in the payload mass.

Fuzzy Logic Control of the End-Point Vibration in an Experimental Flexible Beam

2004 ◽  
Vol 10 (4) ◽  
pp. 493-506 ◽  
Author(s):  
A. Jnifene ◽  
W Andrews
Keyword(s):  
Fuzzy Logic ◽  
Strain Gage ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Angular Displacement ◽  
Angular Position ◽  
Rule Base ◽  
Flexible Beam ◽  
End Point ◽  
Logic Control

This paper is concerned with the design and implementation of a fuzzy logic controller (FLC) to control the end-point vibration in a single flexible beam mounted on a two-degrees-of-freedom platform. The angular position of the hub and the signal from a strain gage mounted on the beam are used as the two inputs to the FLC. In order to add more damping, the strain gage signal is combined with the hub angular velocity represented by the output of a tachometer attached to the motor shaft. We discuss how to build the rule base for the flexible beam based on the relation between the angular displacement of the hub and the end-point deflection, as well as the effect of different scaling gains on the performance of the FLC. We present several experimental results showing the effectiveness of the FLC in reducing the end-point vibration of the flexible beam.

scholarly journals Comparison of Two Distributed Fuzzy Logic Controllers for Flexible-Link Manipulators

2000 ◽  
Author(s):  
Linda Z. Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Joint Angle ◽  
Linear Quadratic Regulator ◽  
The Other ◽  
Flexible Link ◽  
Fuzzy Logic Controllers ◽  
Linear Quadratic ◽  
Distributed Controller ◽  
Logic Controllers

Abstract Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers. An expert in a particular system can usually design a fuzzy logic controller for it easily as can be seen in many applications where fuzzy logic has been already successfully implemented. On the other hand, fuzzy logic controllers are not readily available for flexible-link manipulators. This paper presents two different approaches to design distributed controllers for flexible-link manipulators. The first approach, which is based on observing the performance of flexible manipulators, uses a distributed controller composed of two PD-like fuzzy logic controllers; one controller controls the joint angle while the other controls the tip vibration. The second distributed controller is based on evaluating the importance of the parameters of the system. The most two important parameters, joint and tip point velocities, are grouped together in the same fuzzy logic controller. The other parameters, joint angle and tip point displacement, are used in the second fuzzy logic controller. Both approaches are tuned using nonlinear programming. The paper compares these two approaches with tracking using a linear Quadratic Regulator (LQR).

scholarly journals LQR and Fuzzy Logic Control for the Three-Area Power System

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8522
Author(s):  
Anna Sibilska-Mroziewicz ◽  
Andrzej Ordys ◽  
Jakub Możaryn ◽  
Pooyan Alinaghi Hosseinabadi ◽  
Ali Soltani Sharif Abadi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Frequency Control ◽  
Linear Quadratic Regulator ◽  
Point Of View ◽  
Load Frequency Control ◽  
Linear Quadratic ◽  
Logic Control ◽  
Tie Lines

The three-area power system is widely considered a suitable example to test load frequency control of the distributed generation system. In this article, for such a system, for the power stabilization task, we introduce two controllers: Linear Quadratic Regulator (LQR), which is model-based, and Fuzzy Logic Controller (FLC), which is data-based. The purpose is to compare the two approaches from the point of view of (i) ease of implementation and tuning, and (ii) robustness to changes in the model. The model, together with controls strategies, has been implemented in the MATLAB software. Then, it has been tested for different simulation scenarios, taking into account the disturbances and faulty tie-lines between areas. Various quality measures allow to compare the performance of each control strategy. The comparison in terms of parameter change and load disturbances prompt us to propose suitable metrics and advice notes on the application of each controller.

scholarly journals Design of travel angle control of quanser bench-top helicopter using mamdani-based fuzzy logic controller

2020 ◽  
Vol 17 (2) ◽  
pp. 815 ◽  
Author(s):  
Hasmah Mansor ◽  
Mohamad K. Azmi Mat Esa ◽  
Teddy Surya Gunawan ◽  
Zuriati Janin
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Flat Surface ◽  
Linear Quadratic Regulator ◽  
Performance Comparison ◽  
Test Results ◽  
Linear Quadratic ◽  
Integral Controller ◽  
Better Than

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">This research focuses on travel angle control of a laboratory scale bench-top helicopter developed by Quanser Inc.  Bench top-helicopter is usually used by engineers and researchers to test their designed controllers before applying to the actual helicopter. Bench-top helicopter has the same behavior as the real helicopter, with 3 degree of freedom.  The bench-top helicopter is mounted on a flat surface with two rotors that depends on the voltage supplied to change the direction of the helicopter in 3 different angles. The movement of the helicopter is based on the direction of three-different angles; travel, pitch and yaw angles. The existing Linear Quadratic Regulator-Integral controller used by Quanser Inc has some limitations in terms of tracking capability and settling time; therefore this research is proposed. The objective of this research is to develop Mamdani-based Fuzzy Logic Controller for travel angle control of bench-top helicopter. Performance comparison has been done with the existing Linear Quadratic Regulator-Integral controller in both simulation and hardware. From the test results, it was found that the performance of Fuzzy Logic Controller is better than LQR-I controller especially for closed-loop simulation at desired angle of 30°. The percentage of overshoot of the Fuzzy Logic Controller has been improved from the existing controller which is 4.912% compared to 7.002% for LQR-I.</span>

Dynamic Positioning of An Oceanographic Research Vessel Using Fuzzy Logic Controller in Different Sea States

2021 ◽  
Author(s):  
Kunal Tiwari ◽  
Krishnankutty P.
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Linear Quadratic Regulator ◽  
Research Vessel ◽  
Dynamic Positioning ◽  
Linear Quadratic ◽  
Dynamic Positioning System ◽  
Oceanographic Research ◽  
Thrust Allocation

Abstract A dynamic positioning system is computer controlled system which maintains the positioning and heading of ship by means of active thrust. A DP system consist of sensors, observer, controller and thrust allocation algorithm. The purpose of this paper is to investigate the performance of proportional derivative type fuzzy controller with Mamdani interface scheme for dynamic positioning of an oceanographic research vessel (ORV) by numerical simulation. Nonlinear passive observer is used to filter the noise from the position and orientation. A nonlinear mathematical model of the ORV is subjected to the wave disturbance ranging from calm to phenomenal sea. Robustness and efficiency of the fuzzy logic controller is analysed in comparison with the multivariable proportional integral derivative (PID) and the linear quadratic regulator (LQR) controller. A simplified constrained linear quadratic algorithm is used for thrust allocation. The frequency response of the closed loop system with different controllers is analysed using the bode plot. The stability of controller is established using the Lyapunov criteria.

scholarly journals Analyze and Evaluate the Performance Velocity Control in DC Motor

2020 ◽  
Vol 12 (4) ◽  
pp. 507-516
Author(s):  
Hazim M. Alkargole ◽  
◽  
Abbas S. Hassan ◽  
Raoof T. Hussein ◽  
◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Rule Base ◽  
Velocity Control ◽  
Linear Quadratic ◽  
Proportional Integral ◽  
Motor Controller ◽  
Different Types ◽  
Rule Bases

A mathematical model of controlling the DC motor has been applied in this paper. There are many and different types of controllers have been used with purpose of analyzing and evaluating the performance of the of DC motor which are, Fuzzy Logic Controller (FLC), Linear Quadratic Regulator (LQR), Fuzzy Proportional Derivative (FPD) ,Proportional Integral Derivative (PID), Fuzzy Proportional Derivative with integral (FPD plus I) , and Fuzzy Proportional Integral (FPI) with membership functions of 3*3, 5*5, and 7*7 rule bases. The results show that the (FLC) controller with 5*5 rule base provides the best results among all the other controllers to design the DC motor controller.

scholarly journals Performance Comparisons Of Hybrid Fuzzy-LQR And Hybrid PID-LQR Controllers On Stabilizing Double Rotary Inverted Pendulum

2020 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Jamilu Kamilu Adamu ◽  
Mukhtar Fatihu Hamza ◽  
Abdulbasid Ismail Isa
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Linear Quadratic ◽  
Trajectory Tracking Control ◽  
Stabilization Control ◽  
Full State Feedback ◽  
Lqr Controller ◽  
Rotary Inverted Pendulum

Double Rotary Inverted Pendulum (DRIP) is a member of the mechanical under-actuated system which is unstable and nonlinear. The DRIP has been widely used for testing different control algorithms in both simulation and experiments. The DRIP control objectives include Stabilization control, Swing-up control and trajectory tracking control. In this research, we present the design of an intelligent controller called “hybrid Fuzzy-LQR controller” for the DRIP system. Fuzzy logic controller (FLC) is combined with a Linear Quadratic Regulator (LQR). The LQR is included to improve the performance based on full state feedback control. The FLC is used to accommodate nonlinearity based on its IF-THEN rules. The proposed controller was compared with the Hybrid PID-LQR controller. Simulation results indicate that the proposed hybrid Fuzzy-LQR controllers demonstrate a better performance compared with the hybrid PID-LQR controller especially in the presence of disturbances.

scholarly journals Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

2020 ◽  
Vol 12 (5) ◽  
pp. 1-14
Author(s):  
Noor Salam Al-Fallooji ◽  
◽  
Maysam Abbod
Keyword(s):  
Fuzzy Logic ◽  
Intelligent System ◽  
Moving Average ◽  
Linear Quadratic Regulator ◽  
Optimization Method ◽  
The Other ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Artificial Neural Network Ann ◽  
Yaw Angle

Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.

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