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.

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.

Inverse Dynamics-Based Fuzzy Logic Control of a Projectile Smart Fin

2005 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Surya Kiran Parimi ◽  
Woosoon Yim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Attractive Alternative ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
Novel Approach ◽  
External Moment ◽  
Element Approach

A smart fin for a subsonic projectile should be able to produce maneuvering force and moment that can control its rotation during flight. Piezoelectric actuator is an attractive alternative to usual hydraulic actuators due to its simplicity. The cantilever-shaped actuator can also be fully enclosed within the hollow fin. It has an end fixed to the rotation axle of the fin while the other end is pinned at the tip of the fin. A dynamic model of the system, including external moment due to aerodynamic effects, is obtained using the finite element approach. This paper presents a novel approach for automatically creating fuzzy logic controllers for the fin. This approach uses the inverse dynamics of the smart fin system to determine the ranges of the variables of the controllers. Simulation results show that the proposed controller can successfully drive smart fin under various operating conditions.

A survey of Type-2 fuzzy logic controller design using nature inspired optimization

2020 ◽  
Vol 39 (5) ◽  
pp. 6169-6179
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Prometeo Cortes-Antonio ◽  
Patricia Melin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Optimization Methods ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
Optimal Type ◽  
Logic Controllers

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

Anti-Swing Adaptive Fuzzy Controller for an Overhead Crane With Hoisting

2004 ◽  
Author(s):  
Jamil M. Renno ◽  
Mohamed B. Trabia ◽  
Kamal A. F. Moustafa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Overhead Crane ◽  
Fuzzy Logic Controllers ◽  
Adaptive Fuzzy Controller ◽  
Overhead Cranes ◽  
Logic Control ◽  
Novel Method

This paper presents a novel method for adaptive anti-swing fuzzy logic control for overhead cranes with hoisting. The control action is distributed between three fuzzy logic controllers (FLC’s): trolley controller, hoist controller, and anti-swing controller. A method for varying the ranges of the variables of the three controllers as a function of the crane’s parameters and/or motion variables is presented. Simulation examples show that the proposed controller can successfully drive overhead cranes under various operating conditions.

An Algorithm for Designing Conventional and Fuzzy Logic Control Systems

1997 ◽  
Author(s):  
Dean B. Edwards ◽  
John R. Canning
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Pid Control ◽  
Fuzzy Logic Control ◽  
Design Parameters ◽  
Input State ◽  
State Variables ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
And Control

Abstract This paper presents an algorithm that can be used to design either conventional or fuzzy logic control systems. In order to use the algorithm, the engineer must first choose a performance index for the system which he or she wants to optimize relative to some specified design parameters. For conventional state space controllers, the design parameters are the feedback constants associated with the state variables of the system. For fuzzy logic controllers, the design parameters are the parameters used to define the fuzzy sets for the input state and control variables. We use the algorithm to design proportional plus derivative (PD) and proportional, integral, and derivative (PID) control systems and their equivalent fuzzy logic control systems. The algorithm therefore provides a unifying approach for designing either conventional or fuzzy logic control systems.

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