Error model-oriented vibration suppression control of free-floating space robot with flexible joints based on adaptive neural network

Wenhui, Zhang; Shen, Jinmiao; Ye, Xiaoping; Zhou, Shuhua

doi:10.1016/j.engappai.2022.105028

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…Cui et al [127] used TMM to build the dynamic model of an industrial robot with 6-DOF flexible joints. Zhang et al [128] used Spong's linear-spring assumption and Lagrangian equations to model the dynamics of a flexible-joint space manipulator. Shang et al [129] used AMM to derive the dynamic model for a coupled elastic joint-flexible load drive system, which is composed of a servo motor, an elastic joint, and a flexible load with a tip payload.…”

Section: Modeling Of Joint Flexibilitymentioning

confidence: 99%

Advances in Flexible Robotic Manipulator Systems—Part I: Overview and Dynamics Modeling Methods

Li,

Gao

et al. 2024

IEEE/ASME Trans. Mechatron.

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Flexible robotic manipulators (FRMs) exhibit advantages over traditional rigid-body manipulators, including lower energy consumption, faster response, enhanced safety, reduced spatial footprint, and greater operational flexibility. This article reviews the advancements in the modeling, planning, and control technologies of FRMs, along with their applications and perspectives. Compared with previous survey papers about FRMs, this work has the following highlights. First, modeling methods w.r.t. joint flexibility, link flexibility, and link-joint coupling effects are systematically reviewed; second, FRM motion planning methods are reviewed solely instead of being treated as part of FRM control methods; third, this survey presents the recent advancements in FRMs that incorporate artificial intelligence (AI), acknowledging AI as a prevalent research trend; and finally, this is the only survey to systematically explore the rapidly evolving applications of FRM systems over the past five years. Our work is divided into two independent articles. This article, which is the first one, includes the basic background of FRMs, an overview of previous FRM-related surveys, and dynamic modeling methods for FRMs. The second article will review the prevalent motion planning and control methods for an FRM, together with FRM applications and perspectives.

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Section: Modeling Of Joint Flexibilitymentioning

confidence: 99%

Advances in Flexible Robotic Manipulator Systems—Part I: Overview and Dynamics Modeling Methods

Li,

Gao

et al. 2024

IEEE/ASME Trans. Mechatron.

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show abstract

“…The structure of free-floating planar two link space manipulators with flexible joints is established [22,23], the model is shown in Fig. 1.…”

Section: Dynamic Modeling Of Space Manipulator With Flexible Jointsmentioning

confidence: 99%

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

You¹,

Zhang²,

Shen³

et al. 2023

J. vibroeng.

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Considering the control problems caused by uncertainties such as inaccurate modeling, external disturbance and joint flexibility, a neural network control method based on H∞ is proposed. By establishing the dynamic model of the free-floating space robot with flexible joints, according to its dynamic characteristics, it is split into a slow subsystem model representing the rigid characteristics and a fast subsystem model representing the flexible characteristics. Based on the H∞ robust control theory, a robust controller based on neural network is designed to realize the decoupling control of the rigid dynamic model, The designed weight adaptive learning rate can ensure the online and real-time adjustment of parameters. Based on Lyapunov theory, it is proved that the designed controller can ensure that the L2 gain of the system is less than the given index. A feedback controller based on velocity differential is designed to compensate the angle error caused by joint flexibility. The experimental simulation results verify that the proposed control method is effective and has good engineering application value.

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“…Neural networks are a class of data-driven general-purpose models with powerful nonlinear fitting capabilities, and various neural network control algorithms have emerged and achieved certain results and applications in many fields. [17][18][19][20][21] Although the modeling capability of neural networks in dynamic systems has been studied as early as 1997, 22 there is less research on the use of neural networks in adaptive feedforward control algorithms for the identification of secondary channels due to the influence of the nonlinear nature of neural networks themselves, which makes the update of controller parameters in the Fx-LMS algorithm compromised.…”

Section: Introductionmentioning

confidence: 99%

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

Chun-sheng,

Xue-chun,

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.

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Error model-oriented vibration suppression control of free-floating space robot with flexible joints based on adaptive neural network

Cited by 14 publications

References 29 publications

Advances in Flexible Robotic Manipulator Systems—Part I: Overview and Dynamics Modeling Methods

Advances in Flexible Robotic Manipulator Systems—Part I: Overview and Dynamics Modeling Methods

Adaptive neural network vibration suppression control of flexible joints space manipulator based on H∞ theory

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

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