Design Parallel Fuzzy Partly Inverse Dynamic Method plus Gravity Control for Highly Nonlinear Continuum Robot

Kazeminasab, Meysam; Piltan, Farzin; Esmaeili, Zahra; Mirshekaran, Mahdi; Salehi, Alireza

doi:10.5815/ijisa.2014.01.12

Cited by 20 publications

(31 citation statements)

References 16 publications

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“…Application of the approximate control vector given by (14) leads to the following expression for the closed loop dynamics:…”

Section: Dynamic Formulation Of Continuum Robotmentioning

confidence: 99%

“…Calculate several scale factors are common challenge in classical backstepping controller and fuzzy logic controller, as a result it is used to adjust and tune coefficient. Research on adaptive nonlinear control is significantly growing, for instance, different adaptive fuzzy controllers have been reported in [14]. This paper is organized as follows; second part focuses on the modeling dynamic formulation based on Lagrange methodology, design backstepping control and fuzzy logic methodology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Adaptive Gain Backstepping Technique

Heidari¹,

Shahcheraghi²,

Heidari³

et al. 2015

IJITCS

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Abstract-In this research, intelligent adaptive backstepping control is presented as robust control for continuum robot. The first objective in this research is design a ProportionalDerivative (PD) fuzzy system to compensate the system model uncertainties. The second objective is focused on the design tuning gain adaptive methodology according to high quality partly nonlinear methodology. Conventional backstepping controller is one of the important robust controllers especially to control of continuum robot manipulator. The fuzzy controller is used in this method to system compensation. In real time to increase the system robust fuzzy logic theory is applied to backstepping controller. To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.

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“…Application of the approximate control vector given by (14) leads to the following expression for the closed loop dynamics:…”

Section: Dynamic Formulation Of Continuum Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Adaptive Gain Backstepping Technique

Heidari¹,

Shahcheraghi²,

Heidari³

et al. 2015

IJITCS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on foundation of fuzzy logic methodolo gy; fuzzy logic controller has played important rule to design nonlinear controller for nonlinear and uncertain systems [53][54][55][56][57][58][59][60][61][62][63][64][65][66]. However the application area for fu zzy control is really wide, the basic form for all co mmand types of controllers consists of; …”

Section: Fuzzy Logic Theorymentioning

confidence: 99%

“…A nonlinear methodology is used for nonlinear uncertain systems (e.g., spherical motor) to have an acceptable performance. These controllers divided into six groups, namely, feedback linearizat ion (computed-torque control), passivity-based control, sliding mode control (variable structure control), art ificial intelligence control, lyapunov-based control and adaptive control [56][57]. The main targets in designing control systems are stability, good disturbance rejection to reach the best performance (robustness), and small t racking error [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Rahmani¹,

Piltan²,

Matin³

et al. 2014

IJISA

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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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“…At present, in some applications robot arms are used in unknown and unstructured environment, therefore strong mathematical tools used in new control methodologies to design nonlinear robust controller with an acceptable safety performance (e.g., minimum error, good trajectory, disturbance rejection). According to the control theory, Although the fuzzy-logic control is not a new technique, its application in this current research is considered to be novel since it aimed for an automated dynamic-less response rather than for the traditional objective of uncertainties compensation [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input.…”

Section: Introductionmentioning

confidence: 99%

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

Roshanzamir¹,

Piltan²,

Mozafari³

et al. 2014

IJMECS

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Abstract-In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. In this paper fuzzy model-base adaptive methodology is used to tune the linear Proportional Integral Derivative (PID) controller. The main objectives in any systems are; stability, robust and reliability. However PID controller is used in many applications but it has many challenges to control of continuum robot. To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used. This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.Index Terms-PID Controller, Fuzzy logic methodology, Adaptive Techniques, Model reference fuzzy tuning.

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Design Parallel Fuzzy Partly Inverse Dynamic Method plus Gravity Control for Highly Nonlinear Continuum Robot

Cited by 20 publications

References 16 publications

Intelligent Adaptive Gain Backstepping Technique

Intelligent Adaptive Gain Backstepping Technique

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

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