A hybrid impedance control scheme for underwater welding robots with a passive foundation in the controller domain

Kumar, S. Selva; Rastogi, Vipul; Gupta, Prosenjit

doi:10.1177/0037549717693687

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…For control purposes, the model given by Equations (16)(17)(18)(19) is converted into a global dynamic model by using the transformation matrix presented in Equation 6. The dynamic model of the robot in the global frame can be written as follows:…”

Section: Mathematical Model Extraction From the Integrated Bond Graphmentioning

confidence: 99%

“…A self-adjusting multidisciplinary design of a hydraulic engine mount is studied using the bond graph technique by Karim et al 16 The bond graph is also used for modeling of a Vivo robot for Biospy. 17 Kumar et al 18 proposed a bond graph model for an underwater robot and implemented a hybrid control scheme on the single-arm robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

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Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

2017

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In this paper, a bond graph model of a mobile robot with four Mecanum wheels is developed to extract a dynamic model of the robot. This is achieved using the BG_V21 tool box of MATLAB. The dynamic model thus obtained is used to derive the control law for trajectory tracking by the robot. There are two control algorithms that are used, namely, the flatness-based controller and the backstepping controller. From the simulation results, it is evident that the extracted dynamic model of the robot is accurate. Moreover, the flatness-based controller proved to have the upper hand in performance over the backstepping controller.

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Section: Mathematical Model Extraction From the Integrated Bond Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

2017

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“…As for the manipulator–environment interaction control, the impedance control was proposed [ 21 , 22 ]. Generally speaking, the impedance control can be divided as: typical impedance control, position–force hybrid control and hybrid impedance control [ 23 , 24 , 25 ]. Moreover, the impedance control can also be divided into the Cartesian impedance control and the joint impedance control, and in the Cartesian impedance control, the end-effector force/torque sensor was utilized, and in the joint impedance control algorithms, the joint torque sensor was utilized.…”

Section: Introductionmentioning

confidence: 99%

Research on Hybrid Force Control of Redundant Manipulator with Reverse Task Priority

Sui

Liu

et al. 2022

Materials

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This paper presents the reverse priority impedance control of manipulators with reference to redundant robots of a given task. The reverse priority kinematic control of redundant manipulators is first expressed in detail. The motion in the joint space is derived following the opposite order compared with the classical task priority–based solution. Then the Cartesian impedance control is combined with the reverse priority impedance control to solve the reverse hierarchical impedance controlled, so that the Cartesian impedance behavior can be divided into the primary priority impedance control and the secondary priority impedance control. Furthermore, the secondary impedance control task will not disturb the primary impedance control task. The motion in the joint space is affected following the opposite order and working in the corresponding projection operators. The primary impedance control tasks are implemented at the end, so as to avoid the possible deformations caused by the singularities occurring in the secondary impedance control tasks. Hence, the proposed reverse priority impedance control of manipulator can achieve the desired impedance control tasks with proper hierarchy. In this paper, the simulation experiments of the manipulator will verify the proposed reverse priority control algorithm.

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“…Therefore, many researchers have done lots of researches on the position and force control of the manipulator in recent years. [1][2][3][4][5][6][7][8][9][10] The force control methods of the manipulator include force/position control [1][2][3][4] and impedance control, [5][6][7][8][9][10] but these methods require a wrist force sensor to be installed in the manipulator. In the applications of industrial manipulators, high accurate and reliable force sensors are very expensive, and using force sensors will increase the complexity of electrical, mechanical, and software parts for the manipulator.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, many researchers have done lots of researches on the position and force control of the manipulator in recent years. 1–10…”

Section: Introductionmentioning

confidence: 99%

Decentralized adaptive force/position control of reconfigurable manipulator based on soft sensors

Zhu

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Two soft sensor control methods are proposed to deal with force/position control of reconfigurable manipulator without using wrist force sensors. First, modeling uncertainties and coupled interconnection terms between the subsystems are approximated by using adaptive radial basis function neural network, and the soft sensor model of the contact force is established by means of adaptive radial basis function neural network to design hybrid force/position controller. Then, a decentralized explicit force controller based on impedance inner control is designed. The reference trajectory of impedance inner controller is provided by explicit force controller based on the fuzzy prediction, and the soft sensor model of the contact force is established by the fuzzy system. The proposed soft sensor models do not request the exact mathematical relationship between the contact force and auxiliary variables and provide a feasible method to replace the wrist force sensors which are expensive and easily influenced by the external factors. Compared with the observer method, the proposed soft sensor methods do not depend on the knowledge about the model of reconfigurable manipulator, so provide better position and force tracking precision.

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A hybrid impedance control scheme for underwater welding robots with a passive foundation in the controller domain

Cited by 7 publications

References 16 publications

Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

Research on Hybrid Force Control of Redundant Manipulator with Reverse Task Priority

Decentralized adaptive force/position control of reconfigurable manipulator based on soft sensors

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