Neural network structures for identification of nonlinear dynamic robotic manipulator

Duc, Minh Nguyen; Trong, Thang Nguyen

doi:10.1109/icma.2014.6885935

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…The number of training is 1350. The neural network used for identification as (17), the Gaussian function is described in (28), where the Gaussian function parameters are as shown in:…”

Section: Resultsmentioning

confidence: 99%

“…In studies on robust adaptive control, NN in [17], [26], [27] are mostly used for the unknown nonlinearities as approximation models because of their inherent capabilities of approximation. A simple artificial neural network structure for approximating function may be rewritten as:…”

Section: Building the Neural Network To Compensate For Disturbancementioning

confidence: 99%

“…However, with variability and uncertainty disturbance, the ability to reject interference is still low. To overcome those disadvantage problems mentioned above, in this study, the authors will develop from experiences in building control systems and noise compensation in previous articles [17]- [19] for the construction of the controller of TWSB robot with a nonlinear dynamic structure and uncertainty disturbance. This study's main contributions include designing a sliding mode controller combined with adaptive neural networks with online self-aligning parameters to eliminate the noise and uncertain components in the TWSB robot with nonlinear kinetics and determination of the uncertainty parameters.…”

Section: Introductionmentioning

confidence: 99%

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A neural network combined with sliding mode controller for the two-wheel self-balancing robot

Nguyen¹,

Nguyen

Nguyen³

2021

IJ-AI

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This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

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“…The number of training is 1350. The neural network used for identification as (17), the Gaussian function is described in (28), where the Gaussian function parameters are as shown in:…”

Section: Resultsmentioning

confidence: 99%

Section: Building the Neural Network To Compensate For Disturbancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

Nguyen¹,

Nguyen

Nguyen³

2021

IJ-AI

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“…The general requirement of these intelligent controllers is to reduce the impacts of unknown parameters and unstructured disturbances by utilizing the learning abilities of controlled networks without the need of knowing details about the system during the design phase. This is usually done by approximating a part of the nonlinear model of the system or the whole of it using the learning ability of the controllers [11]. Intelligent controllers has been used and applied successfully in many applications, especially in adaptive control [12][13][14] [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

Ngo¹,

Hoang²,

Tran

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller.

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“…The artificial neural network model is one of machine learning, it learns statistical laws from a large number of training samples to predict unknown laws. Duc et al used a neural network to design a neural network controller that conforms to the robot dynamic characteristics [15]. Deep learning exploits the property that many natural signals are compositional hierarchies, where higher-level features are obtained by composing lower-level ones.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

et al. 2020

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Generally, structural uncertainty of the robot dynamics system refers to model error caused by parameter identification, unstructured uncertainty is the unmodeled dynamic characteristic. No matter how elaborate modeling methods are used, there always be uncertainty. Therefore, this paper applies deep learning for the first time to aid robot dynamic parameter identification of 6 degrees of freedom robot manipulator for compensation of uncertain factors. Firstly, the relatively accurate prediction of torque is obtained by the physical dynamic model using the parameters identification method (errors are less than 10% of the maximum torques). Secondly, we propose a novel deep neural network based approach called Uncertainty Compensation Model (UCM) to compensate the torque error introduced by the uncertainty. The UCM mainly composed by proposed Input Control Module (ICM) and Error Learning Models (ELMs) based on Long-Short-Term Memory and attention mechanism. The proposed ICM, which effectively avoids the unnecessary interference, is used to control valid input for ELMs. The ELMs, consisted by ELM units, concern extracting salient features from sequence data to predict the joint error. Also, this paper summarizes the effects of valid input, timestep and attention mechanism on the performance of the UCM. Finally, the verification of parameter identification and torques compensation is carried out by a Universal Robot 5 manipulator. Compared with the prediction torques of physical dynamic model, the proposed UCM has a good ability to capture the friction characteristics and compensate for the error of local maximum torques, which effectively solves the deficiency of the physical dynamics model and improves prediction accuracy (errors are less than 6% of the maximum torques).

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Neural network structures for identification of nonlinear dynamic robotic manipulator

Cited by 4 publications

References 7 publications

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

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