Design and Tuning of Distributed Importance-Based Fuzzy Logic Controller for Two-Link Rigid-Flexible Manipulator

2003 ◽  
Author(s):  
Linda Z. Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Vertical Plane ◽  
Fuzzy Rules ◽  
Flexible Manipulator ◽  
Linear Controller ◽  
Logic Control ◽  
Single Link

Fuzzy logic control has been widely used in many industrial processes due to its computationally efficient and robust characteristics. In many applications, verbalization of expert-knowledge can be easily used to design a fuzzy logic controller (FLC). On the other hand, other applications with many variables and complex mathematical model offer challenges to fuzzy logic control. Multi-link flexible manipulators belong to this category. An earlier work, [1], presented a distributed importance-based FLC for a single-link flexible manipulator. This paper extends this idea to a two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The structure of the proposed controller is based on evaluating the importance degrees of the variables of the system, over its range of operation, to consider the coupling effects between the rigid and the flexible links. Variables with higher importance degrees are grouped together while variables with lesser importance degrees may be deleted to simplify the design of the controller. After determining the importance degrees of the variables, a distributed controller composed of four two-input one-output FLC’s is created. Unlike the single-link flexible manipulator, the fuzzy rules of the distributed FLC for the two-link rigid-flexible manipulator cannot be written by an expert based on intuition and observation of the inertial system due to the complexity of the manipulator and the coupling effect of its variables. To solve this problem, an importance-based linear controller that has the same input-output structure as that of distributed importance-based FLC is constructed to help write the fuzzy rules of the distributed FLC. Fuzzy rules of the distributed FLC are then selected to mimic the performance of the corresponding linear controllers. To compare the performance of the distributed importance-based FLC with that of importance-based linear controller, these two controllers are tuned using nonlinear programming by varying the gains of the importance-based linear controller and the parameters of membership functions of the variables in the distributed importance-based FLC. Robustness of each of the controllers after tuning is tested by varying the payload of the manipulator. The two importance-based controllers are simulated and compared.

Comparison of Two Distributed Fuzzy Logic Controllers for Two-Link Rigid-Flexible Manipulator

2003 ◽  
Author(s):  
Linda Z. Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Vertical Plane ◽  
Flexible Manipulator ◽  
Computationally Efficient ◽  
Fuzzy Logic Controllers ◽  
Distributed Structure ◽  
Logic Control ◽  
Angular Velocities ◽  
Logic Controllers

Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers. While experts can easily design fuzzy logic controllers (FLC’s) for many applications, some systems such as multi-link flexible manipulators, which have many variables and complex behavior, offer challenges to fuzzy logic control. An earlier work, [1], presented two distributed FLC’s for a single-link flexible manipulator. This paper extends that work to the area of two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The first distributed structure, which is based on observing the performance of the manipulator, uses three PD-like FLC’s. The first two FLC’s control joint angles and joint angular velocities while the third controls the tip vibration. The second distributed structure is based on evaluating the importance degrees of the system output variables of the system by randomly varying its inputs. Variables with the same rank of the importance degree are grouped together and variables with less importance degrees may be deleted to simplify the design of the controller. The fuzzy rules of FLC’s in the two structures are selected to mimic the performance of comparable linear controllers. The parameters in both structures are tuned using nonlinear programming to obtain better performance. The two distributed structures are simulated and compared.

Comparison of Distributed PD-like and Importancebased Fuzzy Logic Controllers for a Two-link Rigid-flexible Manipulator

2005 ◽  
Vol 11 (6) ◽  
pp. 723-747 ◽  
Author(s):  
Linda Z. Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Vertical Plane ◽  
Flexible Manipulator ◽  
Computationally Efficient ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
Distributed Controller ◽  
Angular Velocities ◽  
Logic Controllers

Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers. While experts can easily design fuzzy logic controllers (FLCs) for many applications, some systems such as multilink flexible manipulators, which have many variables and complex behavior, offer challenges to fuzzy logic control. In this paper we present two distributed controllers for a two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The first distributed controller, which is based on observing the performance of the manipulator, uses three PD-like FLCs: the first two FLCs control joint angles and joint angular velocities while the third controls the tip vibration. The second distributed controller is based on evaluating the importance degrees of the output variables of the system. Variables with the same rank of high importance degrees are grouped together, while variables with low importance degrees may be deleted to simplify the design of the controller. The fuzzy rules in the two proposed structures are selected to mimic the performance of comparable linear controllers. The parameters in both FLCs are tuned using nonlinear programming to obtain better performance. The two distributed FLCs are simulated and compared. The robustness of both tuned distributed FLCs is tested by varying the joint trajectories and angular velocities. The effect of changing the payload on the robustness of the two controllers is also considered.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Comparison of a Fuzzy Logic Controller With a P + D Control Law

1989 ◽  
Vol 111 (2) ◽  
pp. 128-137 ◽  
Author(s):  
S. Daley ◽  
K. F. Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Good Control ◽  
Control Law ◽  
Dynamic Coupling ◽  
Process Knowledge ◽  
Logic Control ◽  
Limited Process

A study is described that compares the performance of a self-organizing fuzzy logic control law (SOC) with that of the more traditional P + D algorithm. The multivariate problem used for the investigation is the attitude control of a flexible satellite that has significant dynamic coupling of the axes. It is demonstrated that the SOC can provide good control, requires limited process knowledge and compares favorably with the P + D algorithm.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

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.

scholarly journals Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system

2013 ◽  
Vol 23 (4) ◽  
pp. 395-412 ◽  
Author(s):  
Bidyadhar Subudhi ◽  
Subhakanta Ranasingh
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Fitness Function ◽  
Flexible Manipulator ◽  
Position Tracking ◽  
Flexible Robot ◽  
Robot System ◽  
Single Link ◽  
Tip Position

Abstract This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.

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