Distributed control of modular and reconfigurable robot with torque sensing

Liu, Guangjun; Abdul, S.; Goldenberg, A.A.

doi:10.1017/s0263574707003608

Cited by 99 publications

(28 citation statements)

References 19 publications

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“…Several successful applications of direct joint torque sensing techniques are published in the literatures. [11][12][13] However, using joint torque sensor, measurements are known directly with many drawbacks and may jeopardize the simplicity, reliability, and mechanical ruggedness of the modular robots. For one thing, the strain gauges, which are the most important components of the torque sensors, are known with intensive sensitivity to peripheral temperature variations; for another thing, the sensor data are contaminated with too much noise and unstable states, which are caused by the narrow bandwidth of the torque information.…”

Section: Introductionmentioning

confidence: 99%

Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

Dong

Liu

2017

Advances in Mechanical Engineering

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In this article, a decentralized control strategy is presented for harmonic drive-based modular and reconfigurable robots with uncertain environment contact. Unlike conventional methods that rely on robot-environment contact model or force/torque sensing, this article addresses the problem of controlling modular and reconfigurable robots in contact with uncertain environment using only encoder data of each joint module. By employing a control-oriented harmonic drive model, the dynamic model of modular and reconfigurable robot is formulated as a synthesis of interconnected subsystems, in which the interconnected joint couplings are with small magnitudes. Based on the integral sliding mode control technique and the adaptive super-twisting algorithm, the decentralized controller is designed to compensate model uncertainty in which the up-bound is unknown. The stability of the modular and reconfigurable robot system is proved using Lyapunov theory. Finally, simulations are conducted for 2-degree-of-freedom modular and reconfigurable robots with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

Dong

Liu

2017

Advances in Mechanical Engineering

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“…In the former the dynamic model of each subsystem is approximated to cancel the couplings and in the latter the parameters of distributed proportionalintegral-differential (PID) controllers are adjusted on-line. A decentralized control method for modular and reconfigurable manipulators is developed in [8] based on joint torque measurements for automatic compensation of the coupling effects. A decentralized and robust control method is presented in [9] that increases the performance with respect to a PID controller.…”

Section: Introductionmentioning

confidence: 99%

Automatic centralized controller design for modular and reconfigurable robot manipulators

Giusti

Althoff

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We address the problem of controlling modular robot manipulators. The challenge of modular-robot control is that the overall system dynamics are unknown due to its flexible composition from given modules. Most previous work has faced this problem by designing decentralized controllers. Simple decentralized controllers do not guarantee global asymptotic stability without knowledge of the overall system dynamics and alternative versions involving communication with neighboring modules result in complicated control concepts. Our approach is completely different: we store parameters regarding the dynamics and kinematics of each module and a unique identification number in itself. After finishing the assembly of the modules, the parameters are gathered in a central controller, which also detects the configuration using the identification numbers. Our centralized controller uses this information to synthesize modelbased control laws on-the-fly as if the full system dynamics are known beforehand. We introduce a novel and compact notation to automate this procedure and to generalize the derivation of the kinematic and dynamic model for heterogeneous modules. Finally, a possible application is shown using simulations.

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“…[5][6][7] Numerous studies have been carried out on analytical research of the unconstrained reconfigurable manipulator systems. Liu et al 8 presented a modular distributed control technique for modular and reconfigurable robots based on joint torque sensing, model uncertainties associated with link and payload masses are compensated using joint torque sensor measurement. The remaining model uncertainties, including uncompensated dynamic coupling and joint friction, are compensated by a decomposition-based robust controller.…”

Section: Introductionmentioning

confidence: 99%

Hybrid position–force control for constrained reconfigurable manipulators based on adaptive neural network

Wang

Dong

et al. 2015

Advances in Mechanical Engineering

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This article presents a hybrid position-force control method based on adaptive neural network for addressing the problems of position and force tracking of a constrained reconfigurable manipulator. The reduced-order dynamic model of a reconfigurable manipulator is formulated considering the model uncertainty and the external constraint. Combining decentralized control with centralized control scheme, a hybrid position-force controller is designed for controlling the position and force of the constrained reconfigurable manipulator. The dynamic model uncertainty and the dynamic coupling effect are compensated by radial basis function neural network. The stability of the closed-loop system is proved using the Lyapunov theory. Finally, simulations are performed to study the effectiveness of the proposed method.

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Distributed control of modular and reconfigurable robot with torque sensing

Cited by 99 publications

References 19 publications

Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

Automatic centralized controller design for modular and reconfigurable robot manipulators

Hybrid position–force control for constrained reconfigurable manipulators based on adaptive neural network

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