Sensor-Based Estimation and Control of Forces and Moments in Multiple Cooperative Robots

2004 ◽  
Vol 126 (2) ◽  
pp. 276-283 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Internal Force ◽  
End Effector ◽  
Use Of Data ◽  
Intelligent Behavior ◽  
Internal Forces ◽  
Estimation And Control ◽  
External Forces ◽  
And Control

Control of multiple robots presents numerous challenges, some of which include synchronization in terms of position, motion, force, load sharing and internal force minimization. This paper presents formulation and application of a fuzzy logic based strategy for control of two 6 degree-of-freedom robots carrying an object in a cooperative mode. The paper focuses on control of internal forces that get generated when two or more robots carry an object in coordination. Force/torque (F/T) sensors mounted on wrist of each robot provide the force and torque data in six dimensions. A fuzzy logic controller has been designed to use these force/torque (F/T) data to achieve a cooperating movement in which one robot acts as leader and the other robot follows. The paper also deals with estimation of external forces acting on end effector with the use of data provided by F/T sensors. These external forces and moments are not directly measured by F/T sensor since the quantities measured by F/T sensor are corrupted by the dynamics of the end effector and manipulator (a F/T sensor is usually mounted between wrist and end effector of the robot). This paper investigates the use of Kalman filtering technique to extract the external forces acting on robot end effector utilizing the underlying dynamics of the end effector. Matlab’s Fuzzy logic, Simulink, and State Flow toolboxes are used for achieving real-time, autonomous and intelligent behavior of the two robots. Simulation results from two separate experiments show that the above strategy was able to constrain the internal forces and provide a smooth movement of the manipulators.

Fuzzy Logic in Hole Drilling of Composites

1994 ◽  
Author(s):  
Aditya Thadani ◽  
Athamaram H. Soni
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Thrust Force ◽  
Cutting Speed ◽  
Research Data ◽  
Theoretical Research ◽  
Hole Drilling ◽  
Tool Geometry ◽  
Drilling Process ◽  
And Control

Abstract Experimental and theoretical research data was utilized in building a Fuzzy Logic Controller model applied to simulate the drilling process of composite materials. The objective is to have a better understanding and control of delamination of composites during the drilling process and at the same time to improve the hole finish by controlling fraying and splintering. By controlling the main issues in the drilling process such as feed rate, cutting speed, thrust force, and torque generated in addition to the tool geometry, it is possible to optimize the drilling process avoiding the conventionally encountered problems.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

Modeling and Control Techniques for Microstructure Development

2014 ◽  
Vol 541-542 ◽  
pp. 317-323
Author(s):  
R. Karthikeyan ◽  
R.K. Ganesh Ram ◽  
V. Kalaichelvi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
True Stress ◽  
Microstructure Development ◽  
Modeling And Control ◽  
Control Techniques ◽  
Hot Compression Test ◽  
Strain Data ◽  
Sic Composites ◽  
And Control

True stress-strain data is obtained for 6061Al/ 10% SiC composites by hot compression test. Mathematical models for % volume of recrystallization and diameter of the recrystallized grains are developed with process parameters such as strain, strain rate and temperature. These models are applied for optimization of the grain size and % volume of recrystallization. An attempt has been made to control microstructure evolution during hot deformation using fuzzy logic controller through simulation in MATLAB software. The fuzzy logic controller parameters are tuned using genetic algorithm.

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.

scholarly journals Modelling and Controller Design for Non- Linear Two Tank Interacting System

2020 ◽  
Vol 9 (6) ◽  
pp. 82-85
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Mathematical Modelling ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Linear Process ◽  
Matlab Simulink ◽  
Non Linear ◽  
And Control

This paper explains the mathematical modelling and controller design of Two Tank Interacting System (TTIS) for a non-linear process. To design the non-linear process using Matlab Simulink and control the process using conventional PID controller and Fuzzy Logic Controller (FLC). A comparative study was conducted extensively made to examine which controller suits well for the non-linear process through the response observed.

scholarly journals Optimization and control of water pumping PV systems using fuzzy logic controller

2019 ◽  
Vol 5 ◽  
pp. 853-865 ◽  
Author(s):  
Mustapha Errouha ◽  
Aziz Derouich ◽  
Saad Motahhir ◽  
Othmane Zamzoum ◽  
Najib El Ouanjli ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Pv Systems ◽  
Water Pumping ◽  
Optimization And Control ◽  
And Control

scholarly journals THE MONITORING AND CONTROL OF AN ARM CRANK SYSTEM USING FUZZY LOGIC FOR MULTI-HANDICAPPED YOUTHS WITH MENTAL RETARDATION

2002 ◽  
Vol 14 (05) ◽  
pp. 197-203
Author(s):  
JIANN-SHING SHIEH ◽  
JEN-TSANG HUNG
Keyword(s):  
Fuzzy Logic ◽  
Mental Retardation ◽  
Fuzzy Logic Controller ◽  
Physical Therapist ◽  
Rotation Velocity ◽  
Manual Control ◽  
Control Group ◽  
Monitoring And Control ◽  
Significant Difference ◽  
And Control

In this paper, we try to cope with National Special School in Taoyuan in order to fit the special requirements for multi-handicapped youths with mental retardation using current high technology, such as microcomputer control, sensing technology, and fuzzy logic control. The prototype device of this paper is focused on designing an arm crank system using a fuzzy logic controller (FLC) to exercise for the upper limb. The rotation velocity of this system was controlled via adjusting the electric current of the brake in order to increase the training motivation. Hence, a FLC was designed into this system in comparison with a manual control by an expert (i.e., physical therapist). Fifteen multi-handicapped youth with mental retardation were treated using the manual control as a control group, and another fifteen youths were treated using FLC as an experimental group. The root mean square deviation (RMSD) of the change of rotation velocity of arm crank was no significant difference (p > 0.05) using Student t test analysis. It means that FLC can be replaced the manual control by the qualified physical therapist. Therefore, it can be seen as a demonstration of feasibility of the applicability of this FLC for monitoring and control the velocity of the arm crank. But, it still needs a longer series of multi-handicapped youths with mental retardation, perhaps to refine the rule-bases, and certainly to see how widely they are applicable.

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