Ga-Neuro-Fuzzy Control of Flexible-Link Manipulators

Siddique, M. N. H.; Tokhi, M. O.

doi:10.3182/20020721-6-es-1901.00969

Cited by 8 publications

(10 citation statements)

References 11 publications

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“…Hybrid learning algorithms have been Fig. 10 Structure of the NN extensively described in the literature [35][36][37][38][39][40][41][42][43][44][45][46]. In the forward pass of the hybrid learning algorithm, the node outputs increase until layer 4 and the consequent parameters are identified by the leastsquares method.…”

Section: Hybrid Learning Algorithmmentioning

confidence: 99%

Modelling of neurofuzzy control of a flexible link

Tınkır

Önen

Kalyoncu

2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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A modelling approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program is proposed. Initially, a CAD model of the flexible link is created using experimentally determined values of system parameters. This CAD model is then exported to MATLAB software and the Simulink/ SimMechanics toolbox. An adaptive-network-based fuzzy logic controller is used for position and vibration control of the flexible link.Experimental and simulation results are presented that validate the proposed approach.

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Section: Hybrid Learning Algorithmmentioning

confidence: 99%

Modelling of neurofuzzy control of a flexible link

Tınkır

Önen

Kalyoncu

2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Among them, genetic algorithm (GA) has received great attention [16,17]. However, recent research has identified some deficiencies in GA performance [18,30].…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimized hybrid fuzzy precompensated trajectory control of rigid-flexible manipulator

Alavandar

Jain

Nigam

2009

KES

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Light-weight flexible arms will most likely constitute the next generation robots due to their large payload carrying capacities at high speeds and less power demand. Control problem of robots with flexible members is more complex compared to rigid robots due to vibrations during the motion. This paper presents the optimization technique inspired by social behavior of bird flocking to optimize hybrid Fuzzy Precompensated Proportional -Derivative (PD) controller in trajectory control of two link rigid-flexible manipulator. Numerical simulation using the dynamic model of two link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems and the use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm based optimization is presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using hybrid Fuzzy Precompensated Proportional -Derivative controller with particle swarm optimization.

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“…A flexible joint robot arm has distributed parameters that are characterized by an infinite order system and utilizes flexible materials. Due to the nimble virtues of flexible manipulators, it's very complicated to do mathematical modeling and subsequent model-based control of the system [8].…”

Section: Introductionmentioning

confidence: 99%

Investigation of feasible controller for position control of flexible joint manipulator using multiple control techniques

2019

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With the advancement in technology, industries are moving from automation to robotics in the era of robotization. Different control techniques are being used in a different sector of production to control the whole machinery with special attention towards control the position and vibrations. In this research article, the feasible controller has been investigated for position control of flexible joint manipulator. For this, an effective version of a single linked flexible joint robotic manipulator has been used as a platform to control the position of manipulator using different control approaches. Euler's-Lagrange equations have been used to obtain the effective version of the system and the position control has been performed by proportional-integral-derivative (PID) controller, pole -placement and linear quadratic regulator (LQR) methods. The main target of this research work is to maintain the rotational angle of the joint at an appropriate position and to remove the fluctuations at a specific robotic tool which is known as end effectors. The gains of PID controller have been delineated with the help of genetic algorithm and tuned for obtaining the most suitable gains to control the position of flexible joint manipulator and vibrations. As the settling time of the system was high so genetic algorithm has been used to optimize it. The Matlab ® /simulation results depict that genetic algorithm tuned PID controller performed better correlated to both pole placement and LQR method of control.

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Ga-Neuro-Fuzzy Control of Flexible-Link Manipulators

Cited by 8 publications

References 11 publications

Modelling of neurofuzzy control of a flexible link

Modelling of neurofuzzy control of a flexible link

Particle swarm optimized hybrid fuzzy precompensated trajectory control of rigid-flexible manipulator

Investigation of feasible controller for position control of flexible joint manipulator using multiple control techniques

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