Theoretical and Experimental Studies of Iterative Learning control of Underwater robot manipulators

Sakagami, Norimitsu; Inoue, Manabu; Kawamura, Sadao

doi:10.2534/jjasnaoe1968.2002.411

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…In [27], the authors simply applied the iterative learning control to an underwater robotic system to address the issue that the parameter approximation of hydrodynamic coefficients of the underwater robotic system is unsuited to establish the feedforward control inputs due to the difficulty in modeling and approximating the hydrodynamic terms. The advantages of the iterative learning control are illustrated by the experimental results.…”

Section: Iterative Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

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Learning control for robotic manipulators has been developed over the past decade and to the best of the authors' knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

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Section: Iterative Learning Control and Its Variantsmentioning

confidence: 99%

On the Development of Learning Control for Robotic Manipulators

Zhang

Wei

2017

Robotics

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“…With the acceleration of ocean development process, researches on the underwater robots which can explore unknown underwater environment and perform specific underwater tasks also draw the wide attention of research institutions. As the carrier for working in the complicated ocean environment, autonomy and security are the important features of underwater robots, and intelligent control technology is the important foundation and technology for ensuring its autonomy and security [1,2]. To cater to the demands of ocean development such as marine oil exploitation etc., the development and application work for underwater robot have been actively conducted in the recent years.…”

Section: Introductionmentioning

confidence: 99%

Research on the Dynamic Vibration Control of Underwater Robot

Li-ya¹,

Zhao²

2015

TOAUTOCJ

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With mankind's increasing development and utilization of the ocean, underwater robot which can detect underwater environment and autonomously accomplish specific tasks has drawn great attention of researchers. Research was conducted on the kinematic and dynamical control of the underwater robot with six degrees of freedom. Firstly, considering the influences of gravity, buoyancy force, thrust and hydrodynamic force, the kinematic model and dynamical model of the underwater robot were built to describe the complicated underwater behaviors of the robot. Based on this, the dynamic model was simplified and model prediction control of the dynamics was conducted. Comprehensive comparison was also conducted on the results, which showed that model prediction control can reduce the vibration response of the system effectively.

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“…An iterative learning approach to estimation of the underwater manipulator dynamics under repeatable disturbances was proposed by Sakagami et al [7]. Under conditions where it is difficult to implement iterative learning, it is possible to apply time scale interpolation techniques [8].…”

Section: Introductionmentioning

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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Theoretical and Experimental Studies of Iterative Learning control of Underwater robot manipulators

Cited by 6 publications

References 2 publications

On the Development of Learning Control for Robotic Manipulators

On the Development of Learning Control for Robotic Manipulators

Research on the Dynamic Vibration Control of Underwater Robot

A neuro-fuzzy controller for underwater robot manipulators

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