A robotic system based on neural network controllers

Sánchez, Leopoldo; Marichal, G.N.; Moreno, L.; Rodrigo, J.J.; Hamilton, A.; Méndez, J. Hernández

doi:10.1016/s0954-1810(99)00012-6

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…Therefore, the decentralized form of neural network compensator is adopted here. Decentralized implementations of neural network-based controllers have been shown to have bounded tracking errors for interconnected nonlinear systems with strong interactions [19], and precise tracking performance in uncertain systems such as robot manipulators [20].…”

Section: Dynamics Of Underwater Manipulatorsmentioning

confidence: 99%

A neuro-fuzzy controller for underwater robot manipulators

Pandian

Sakagami

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Autonomous underwater vehicles are increasingly replacing the prevalent remotely operated vehicle-manipulator systems. Most current generation AUVs are not fitted with manipulators and hence are mainly limited to underwater surveying and surveillance tasks because of the difficulty in the coordinated control of the resulting underwater vehiclemanipulator systems. While several researchers have proposed various techniques for control of AUVs, there is still much research to be done on the precise control of underwater manipulators. This paper presents an intelligent control method for underwater manipulators based on the neuro-fuzzy approach. The controller is composed of fuzzy PD control with feedback gain tuning by linguistic rules. A neural network compensator approximates the dynamics of the multiple degrees of freedom manipulator in decentralized form. The proposed controller has advantages of simplicity of implementation due to decentralized design, precision, and robustness to payload variations and hydrodynamic disturbances. It has lower energy consumption compared to the conventional PD control method. The effectiveness of the proposed controller is illustrated by experimental results for a three degrees of freedom underwater manipulator.

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Section: Dynamics Of Underwater Manipulatorsmentioning

confidence: 99%

A neuro-fuzzy controller for underwater robot manipulators

Pandian

Sakagami

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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“…In the presented case, a neurofuzzy strategy has been applied, although at the beginning of the work other intelligent strategies were considered. One of the considered techniques was the neural networks (Acosta et al, 1999). However, its inherent saturation problems make them an inadequate method, at least in the application under consideration.…”

Section: Neuro-fuzzy Approachmentioning

confidence: 99%

A neuro-fuzzy method applied to the motors of a stereovision system

Marichal¹,

Toledo²,

Sánchez³

et al. 2007

Engineering Applications of Artificial Intelligence

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“…The simulation software includes dynamics and kinematics equations for the given robot model. The equations for the motion of the robotic manipulator were developed by the direct application of classical Newtonian mechanics [21,24,25]. For a manipulator with m joints, the mathematical model, consisting of a set of coupled nonlinear differential equations, can be expressed in the joint coordinate system as follows:…”

Section: Robot Model Used In the Simulationsmentioning

confidence: 99%

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

Köker

2006

Information Sciences

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A robotic system based on neural network controllers

Cited by 18 publications

References 7 publications

A neuro-fuzzy controller for underwater robot manipulators

A neuro-fuzzy controller for underwater robot manipulators

A neuro-fuzzy method applied to the motors of a stereovision system

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

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