Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model

Sun, Changyin; He, Wei; Hong, Jin‐Woo

doi:10.1109/tsmc.2016.2562506

Cited by 282 publications

(114 citation statements)

References 66 publications

(35 reference statements)

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“…In [92], the NN was applied for the estimation of the unknown model parameters of a marine surface vessel and in [93] the full-state constraint of an n-link robotic manipulator was achieved by using the NN control. The NN controller was also constructed for flexible robotic manipulators to deal with the vibration suppression based on a lumped spring-mass model [94] while in [95], two RBFNNs were constructed for flexible robot manipulators to compensate for the unknown dynamics and the dead-zone effect, respectively.…”

Section: Nn Based Robot Control With Input Nonlinearitiesmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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Section: Nn Based Robot Control With Input Nonlinearitiesmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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“…The boundary control is proposed based on smooth hyperbolic tangent function to stabilize the string system and saturate the control input, and the disturbance observer is exploited to track the external disturbance. In subsequent work, to achieve the transient performance regulation of the control systems based on neural networks approaches as presented in , we will devote to investigating the control design based on truncation model approach.…”

Section: Control Designmentioning

confidence: 99%

Boundary Control for a Vibrating String System with Bounded Input

Zhao

Liu

Luo

2017

Asian Journal of Control

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In this study, we deal with the control problem of a vibrating string system under the condition of restricted input and external disturbance. The major objectives are the development of a boundary vibration control scheme for globally stabilizing the string system and simultaneously for compensating the effect of the input saturation, and the design of a disturbance observer for tracking the external disturbance. To this end, a boundary control is proposed based on smooth hyperbolic function to suppress the vibration and eliminate the input restriction effect, and a disturbance observer is employed to mitigate the external disturbance. The asymptotic stability of the controlled system is demonstrated employing the extended LaSalle's invariance principle. In order to verify the control performance of the proposed control, simulation results are presented by the choice of proper control design parameters.

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“…In modern industry field, significant research efforts have been made for the modeling and control of flexible manipulators due to their higher precision, faster operation, and lower energy consumption compared with typical rigid-link robotic arms. [1][2][3][4][5][6] However, such flexible structures may oscillate in the presence of exoteric factors and the elastic deformation caused by the motion of flexible arms will reduce the control performance. As a result, various methods on system modeling and control design, such as the singular perturbation method, model predictive control scheme, and backstepping control method, have been developed for flexible manipulators.…”

Section: Introductionmentioning

confidence: 99%

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

Xing

Liu

2019

Intl J Robust & Nonlinear

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Summary In this brief, this paper deals with a robust adaptive iterative learning control (ILC) problem for a flexible manipulator attached to a moving vehicle with uncertainties. To begin with, considering the infinite dimensionality of the flexible distributed parameter system, a coupled ordinary differential equation and partial differential equation model is established without any discretization. Then, it is followed by a presentation of an adaptive ILC strategy, which can drive the vehicle and joint to the desired positions based on a proportional‐derivative feedback structure with unmodeled dynamics and external disturbances. The deformation of the flexible manipulator can also be suppressed simultaneously under the proposed control laws. By using Lyapunov's direct method, the stability of the closed‐loop system is demonstrated. The simulation results are provided to illustrate the effectiveness of the proposed control laws.

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Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model

Cited by 282 publications

References 66 publications

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Boundary Control for a Vibrating String System with Bounded Input

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

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