Dynamic Parameter Identification for Reconfigurable Robot Using Adaline Neural Network

Ge, Weimin; Wang, Bingda; Mu, Haozhi

doi:10.1109/icma.2019.8816533

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Although linear models are attractive for many reasons, they have their limitations, especially since all physical systems are nonlinear to some extent, and in many cases linear models are not suitable for representing these systems. In this regard, there is currently a significant interest in methods for identifying nonlinear systems, especially using machine learning methods based on neural networks [16][17][18].…”

Section: Problem Statement Of the Control Object Identification By An...mentioning

confidence: 99%

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Prokopiev

Shmalko

Diveev

2023

15th International Conference &Amp;ldquo;Intelligent Systems” (INTELS’22)

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Section: Problem Statement Of the Control Object Identification By An...mentioning

confidence: 99%

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Prokopiev

Shmalko

Diveev

2023

15th International Conference &Amp;ldquo;Intelligent Systems” (INTELS’22)

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“…Atkeson et al 4 developed the least square algorithm to estimate load and link inertial parameters of a PUMA 600 industrial robot. Wang et al 5 built the Adaline neural network model to estimate the dynamic parameter for the reconfigurable robot. Taking into account the measurement errors, Olsen and Petersen 6 presented the maximum likelihood estimation (MLE) for estimating inertial parameter.…”

Section: Introductionmentioning

confidence: 99%

External force estimation for robotic manipulator base on particle swarm optimization

Liu

Wang

2021

International Journal of Advanced Robotic Systems

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The safe disposal of nuclear waste in radioactive environment urgently needs cost-effective approaches. Toward this goal, this article developed a method to external force estimation based on the identified model without force sensors. Firstly, the mathematical model including joint friction was obtained and transformed into the linear combination of unknown parameter to be estimated. Secondly, the unknown parameters were identified based on the improved particle swarm optimization algorithm, the identification procedure was implemented by optimizing the excitation trajectories to excite joint motion and sampling relevant data. Identified results were compared with the biogeography-based optimization algorithm and the cuckoo search algorithm. Then, the identified dynamic parameter was applied to external force estimation. Finally, the verification of external force estimation has been carried out using the Kinova Jaco2 robot manipulator, and the experimental results showed that the external forces by the proposed method could be estimated with an root mean square error of 0.7 N.

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“…Machine learning has the nonlinear fitting ability, which is used to solve the problem of robot parameter identification. Wang et al established a shallow neural network to identify the dynamic parameters of the reconfigurable robot [13]. In terms of identification strategy, The one-step identification method easily overwhelms joint parameters with small torque coefficients due to joint coupling.…”

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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Dynamic Parameter Identification for Reconfigurable Robot Using Adaline Neural Network

Cited by 9 publications

References 14 publications

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

External force estimation for robotic manipulator base on particle swarm optimization

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

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