Fast Fractional-Order Terminal Sliding Mode Control With RBFNN Based Sliding Perturbation Observer for 7-DOF Robot Manipulator

Wang, Jie; Cheol, Lee Min; Kim, Jae-Hyung; Hee, Kim Hyun

doi:10.1109/access.2021.3075697

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…Zhang W utilized the fractional-order method in the proposed non-singular fast terminal sliding mode controller to improve the tracking performance of the controller and designed RBFNN to approximate the unknown nonlinear function of the system to accomplish model-free control [ 31 ]. Jie W proposed a fast fractional-order terminal sliding mode controller with a new perturbation estimator that applied the data-driven method RBFNN to compensate for the estimation error in the conventional sliding perturbation observer to improve the tracking accuracy and reduce the chattering [ 32 ]. Kim SJ proposed an adaptive robust RBFNN non-singular terminal sliding mode controller to reduce swinging in the snake robot’s head where the RBFNN compensates for interference and an adaptive robust term to make up for the shortcomings of neural network control to eliminate system chattering [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

Fang,

Mao

2024

Sensors

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Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other’s disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness.

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Section: Related Workmentioning

confidence: 99%

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

Fang,

Mao

2024

Sensors

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“…The hidden layer contains several neurons activated by a nonlinear radial basis function. The output layer contains a linear neuron (Zhu et al , 2016; Wang et al , 2021). The structure of estimator based on RBFNN is shown in Figure 2.…”

Section: Friction Estimation Including Nonlinear Load Effectmentioning

confidence: 99%

A nonlinear robot joint friction compensation method including stick and sliding characteristics

Feng

Zhang

et al. 2023

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Purpose Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot. Design/methodology/approach This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving. Findings Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods. Originality/value The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

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“…Model-based approaches for industrial robot manipulators such as advanced controller and observer design [1], [2], human-robot/robot-environment interaction [3], [4] and simulation of virtual robot motion [5], [6] all rely on accurate dynamic models. However, modeling accuracy of a dynamic model depends on its sensitivity with respect to environmental noise, especially non-Gaussian noise commonly seen in measurements [7].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Huang,

Cheng,

Huang

2023

IEEE Access

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A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into the formulation of linear matrix inequality (LMI) to examine the physical consistency of the base parameters. The optimization problem of LMI is then solved by a matrix-oriented gradient-type RNN. Since the network structure of this type of RNN is simple and the process for solving the optimization problem of LMI is parallel distributed, the physical feasibility test can be completed more quickly than when utilizing commonly used semi-definite programming techniques. By taking advantage of highly efficient computation capabilities, the proposed physical feasibility test algorithm is particularly suitable for identification approaches that require rapid feasibility assessment such as on-line identification methods or optimization-based identification methods. An evolutionary algorithm-based identification method is used as an illustrative example to assess the performance of the proposed approach. In addition, a stability proof for the proposed gradient-type RNN is also provided. Results of the experiments conducted on a 6-DOF industrial robot manipulator verify the effectiveness of the proposed approach.INDEX TERMS Dynamic parameter identification, industrial robot manipulator, physical feasibility, recurrent neural network.

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Fast Fractional-Order Terminal Sliding Mode Control With RBFNN Based Sliding Perturbation Observer for 7-DOF Robot Manipulator

Cited by 9 publications

References 28 publications

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control

A nonlinear robot joint friction compensation method including stick and sliding characteristics

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

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