Neuro-fuzzy-genetic controller design for robot manipulators

Mester, Gyula

doi:10.1109/iecon.1995.483338

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…When the autonomous mobile robotic platform is moving towards the target and the sensors detect an obstacle, an avoiding strategy is necessary [3][4][5][6][7][8][9][10][11][12][13]. While the autonomous mobile robotic platform is moving it is important to compromise between [13]:  avoiding the obstacles and  moving towards the target position.…”

Section: Strategy Of Autonomous Anthropomorphic Wheeled Mobile Robotimentioning

confidence: 99%

Development of Autonomous Anthropomorphic Wheeled Mobile Robotic Platform

Mester

2018

Interdiscip. Descr. Complex Syst.

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This article presents the intelligent autonomous anthropomorphic wheeled mobile robotic platform motion control in unstructured environments. The fuzzy control of a wheeled autonomous anthropomorphic mobile robotic platform motion in unstructured environments with obstacles is proposed. Outputs of the fuzzy controller are the angular speed difference between the left and right wheels of the autonomous anthropomorphic robotic platform and the robot velocity. The simulation results show the effectiveness and the validity of the obstacle avoidance behaviour in the unstructured environment and velocity control of autonomous anthropomorphic mobile robotic platform motion of the proposed fuzzy control strategy. Wireless sensor-based remote control of autonomous anthropomorphic mobile robotic platform motion in unstructured environments is proposed.

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Section: Strategy Of Autonomous Anthropomorphic Wheeled Mobile Robotimentioning

confidence: 99%

Development of Autonomous Anthropomorphic Wheeled Mobile Robotic Platform

Mester

2018

Interdiscip. Descr. Complex Syst.

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“…Mohammadian and Stonier developed a fuzzy logic controller and optimized the membership functions by genetic algorithm [13]. Mester in [14] developed a neuro-fuzzy-genetic controller for robot manipulators by applying the genetic algorithm to optimize the fuzzy rule set. Eskil and Efe and Kaynak in [15] proposed a procedure for T-Norm adaptation in fuzzy logic systems using genetic algorithm, they investigate the performance of fuzzy system having parameterized T-Norm in control of robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Based Tracking Control of 3 DOF Robot Arm

Alavandar

Nigam

2008

2008 First International Conference on Emerging Trends in Engineering and Technology

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The essential problem in controlling robots is to make the manipulator follow a desired trajectory. This paper presents the Genetic algorithm tuned Fuzzy PID controller (GAFPID) to follow the desired trajectory for a three degree of freedom (DOF) robot arm. Numerical simulation using the dynamic model of three DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PD, PID and Fuzzy PID controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using the proposed controller than conventional controller.

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“…Mohammadian and Stonier developed a fuzzy logic controller and optimized the membership functions by genetic algorithm [8]. Mester in [9] developed a neurofuzzy-genetic controller for robot manipulators; he applied the genetic algorithm to optimize the fuzzy rule set. Eskil and Efe and Kaynak in [10] proposed a procedure for T-Norm adaptation in fuzzy logic systems using genetic algorithm, they investigate the performance of fuzzy system having parameterized T-Norm in control of robot manipulators.…”

Section: Introductonmentioning

confidence: 99%

Design of Mixed Fuzzy-GA Controller For SCARA Type Robot

Eftekharian

Salarieh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a Mixed Fuzzy-GA Controller (MFGAC) for trajectory tracking of an industrial Selective Compliance Assembly Robot Arm (SCARA), which is one of the most employed manipulators in industrial environments. In this robot nonlinear effects due to centrifugal, coriolis and internal forces are more important than friction and gravity forces, unlike most industrial robots. The control procedure of MFGAC is consisting of a mixed fuzzy controller which is optimized by genetic algorithm. In this work we first design a Traditional Fuzzy Controller (TFC) from the viewpoint of a Single-Input Single-Output (SISO) system for controlling each degree of freedom of the robot. Then, an appropriate coupling fuzzy controller is also designed according to the characteristics of robot's dynamic coupling and incorporated into a TFC. After that by using genetic algorithm we tune and optimize the membership functions and scaling factors of designed fuzzy controller. This control strategy can not only simplify the implementation problem of fuzzy control, but also improve control performance.Index terms -Intelligent control, Mixed fuzzy controller, SCARA robot, Genetic algorithm. INTRODUCTONThe main difference between Multiple-Input MultipleOutput (MIMO) control systems and SISO control systems is in the means of estimating and compensating for the interaction between the degrees of freedom. MIMO systems usually posses complicated dynamics coupling. Estimating the accurate dynamic model and decoupling it for designing the controller is difficult. Hence the traditional model-based SISO control scheme is hard to implement on complicated MIMO systems because the computational burden is large. Therefore, the model-free intelligent control strategy is gradually attracting attention.Although fuzzy control theory has been successfully employed in many control engineering fields [1-5], its control strategies were mostly designed for SISO systems. Also, the number of control rules and controller computational burden grow exponentially with the number of variables considered. Clearly, the difficulty in controlling MIMO systems is how to solve the coupling effects between the degrees of freedom. Therefore, an appropriate coupling fuzzy controller is incorporated into a TFC for controlling MIMO robotic system. After that we investigate the use of genetic algorithm in the design and optimization of MFC.Recently, many works have been done using GA to optimize membership functions of Fuzzy Logic Controller (FLC). Karr, for example, has used a GA to generate membership functions for a PH control process [6] and cartpole problem [7]. Mohammadian and Stonier developed a fuzzy logic controller and optimized the membership functions by genetic algorithm [8]. Mester in [9] developed a neurofuzzy-genetic controller for robot manipulators; he applied the genetic algorithm to optimize the fuzzy rule set. Eskil and Efe and Kaynak in [10] proposed a procedure for T-Norm adaptation in fuzzy logic systems using genetic algorithm, they investigate the ...

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Neuro-fuzzy-genetic controller design for robot manipulators

Cited by 8 publications

References 11 publications

Development of Autonomous Anthropomorphic Wheeled Mobile Robotic Platform

Development of Autonomous Anthropomorphic Wheeled Mobile Robotic Platform

Genetic Fuzzy Based Tracking Control of 3 DOF Robot Arm

Design of Mixed Fuzzy-GA Controller For SCARA Type Robot

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