Tuning and optimisation of membership functions of fuzzy logic controllers by genetic algorithms

Mohammadian, Masoud; Stonier, R.J.

doi:10.1109/roman.1994.365903

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…Its properties are precisely explained in [14,18]. In our study, the objective function for the optimization problem is related to the equation (2) given in section 3.…”

Section: Problem Formulationmentioning

confidence: 99%

“…To enhance the system performance, several studies proposed a GA optimization of fuzzy logic controller [4,14,18]. The aims of applications were to find an optimum trajectory for a truck back upper problem [14], to minimize DC-link voltage variations [4], and to improve the performance of an industrial process [18]. However in [4], the optimization problem assumed symmetrical trapezoidal and triangular MFs and in [18], it supposed an isosceles-triangle MFs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Methodology for technical and economic assessment of electric vehicles integration in distribution grid

Bouallaga

Davigny

Courtecuisse

et al. 2017

Mathematics and Computers in Simulation

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“…Its properties are precisely explained in [14,18]. In our study, the objective function for the optimization problem is related to the equation (2) given in section 3.…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology for technical and economic assessment of electric vehicles integration in distribution grid

Bouallaga

Davigny

Courtecuisse

et al. 2017

Mathematics and Computers in Simulation

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“…Fuzzy controller can characterize better behavior comparing with classical linear PID controller because of its non linear characteristics. Recently, fuzzy-logic and conventional-techniques have been combined (hybrid) to design FL controllers which pave to appropriate solution for controlling the robot manipulators [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

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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“…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.…”

mentioning

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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Tuning and optimisation of membership functions of fuzzy logic controllers by genetic algorithms

Cited by 14 publications

References 5 publications

Methodology for technical and economic assessment of electric vehicles integration in distribution grid

Methodology for technical and economic assessment of electric vehicles integration in distribution grid

Genetic Fuzzy Based Tracking Control of 3 DOF Robot Arm

Design of Mixed Fuzzy-GA Controller For SCARA Type Robot

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