A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator

Piltan, Farzin; Rafaati, Mohammad Javad; Khazaeni, Fatima; Hosainpour, Ali; Soltani, Samira

doi:10.5815/ijieeb.2013.01.02

Cited by 28 publications

(34 citation statements)

References 9 publications

(5 reference statements)

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“…It has as sumed that the desired motion trajectory for the manipulator ( ) , as determined, by a path planner. Defines the tracking error as [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]:…”

Section: (4)mentioning

confidence: 99%

“…This method is free of some model techniques as in model-based controllers. As mentioned that fuzzy logic application is not only limited to the modelling of nonlinear systems [31][32][33][34][35][36] but also this method can help engineers to design a model-free controller. Control spherical motor using model-based controllers are based on man ipulator dynamic model.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic control has played an important role in advance science and engineering and its extreme importance in many industrial applications, i.e., aerospace, mechanical engineering and joint control. The first significant work in automatic control was James Watt"s centrifugal governor for the speed control in motor engine in eighteenth century [29][30][31][32][33][34][35][36][37][38][39][40]. There are several methods for controlling a spherical motor, wh ich all of them fo llo w two co mmon goals, namely, hardware/software imp lementation and acceptable performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Rahmani¹,

Piltan²,

Matin³

et al. 2014

IJISA

Self Cite

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Abstract-Spherical three Degree-of-Freedom (DOF) is controlled by model-base fuzzy computed torque controller. The spherical motor has three revolute joints allowing the corresponding parts to move horizontally and vertically. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system"s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Computed torque controller (CTC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known, CTC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback Inference Engine (FIS) is used to reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a M ATLAB based area. Based on this research model-base fuzzy computed torque controller applied to spherical motor is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.

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Section: (4)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Rahmani¹,

Piltan²,

Matin³

et al. 2014

IJISA

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is free of some model techniques as in modelbased controllers. As mentioned that fuzzy logic application is not only limited to the modelling of nonlinear systems [31][32][33][34][35][36] but also this method can help engineers to design a model-free controller. Control spherical motor using model-based controllers are based on manipulator dynamic model.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic control has played an important role in advance science and engineering and its extreme importance in many industrial applications, i.e., aerospace, mechanical engineering and joint control. The first significant work in automatic control was James Watt"s centrifugal governor for the speed control in motor engine in eighteenth century [29][30][31][32][33][34][35][36][37][38][39][40]. There are several methods for controlling a spherical motor, which all of them fo llow two co mmon goals, namely, hardware/software implementation and [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Design New Intelligent-Base Chattering Free Nonlinear Control of Spherical Motor

Tayebi¹,

Piltan²,

Piltan³

et al. 2014

IJISA

Self Cite

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Abstract− The main four objectives to design controllers are: stability, robust, minimum error and reliability. Linear PID controller is model-free controller and this controller is not reliable. One of the robust nonlinear controller to control of nonlinear systems is sliding mode controller (SM C). Sliding mode controller (SM C) is robust conventional nonlinear controller in a partly uncertain dynamic system"s parameters. Sliding mode controller is divided into two main sub parts: discontinues controller and equivalent controller . Discontinues controller is used to design suitable tracking performance based on very fast switching. Fast switching or discontinuous part have essential role to achieve to good trajectory following, but it is caused system instability and chattering phenomenon. Chattering phenomenon is one of the main challenges in conventional sliding mode controller and it can causes some important mechanical problems such as saturation and heats the mechanical parts of robot manipulators or drivers. To reduce or eliminate the chattering two methods are used in many researches which these methods are: boundary layer saturation method and artificial intelligence based method. In this research fuzzy switching methodology is used to eliminate the chattering in presence of uncertainty to increase the robust of this controller with application to three dimensions of spherical motor.

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Design of a fuzzy controller for delayed and high-order systems using particle swarm optimization

Rezaei

Bakhoda

Menhaj

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator

Cited by 28 publications

References 9 publications

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Design New Intelligent-Base Chattering Free Nonlinear Control of Spherical Motor

Design of a fuzzy controller for delayed and high-order systems using particle swarm optimization

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