Robust least square estimation of the CRS A465 robot arm’s dynamic model parameters

Kinsheel, Azeddien

doi:10.5897/jmer11.061

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…For joint angular velocity, owing to the presence of significant noise in the collected data, the improved Fourier series is fitted using the LS method to the filtered joint angle data. Then, the first and second derivatives of the fitted Fourier series are calculated to obtain the angular velocity and acceleration of the six joints (Kinsheel et al , 2012).…”

Section: Methodsmentioning

confidence: 99%

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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Purpose Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque. Design/methodology/approach The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied. Findings The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved. Originality/value PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

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Section: Methodsmentioning

confidence: 99%

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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“…However, this method has very strict requirement for robot dynamics. About dynamics parameters, there are disassembly measurement method, CAD method and overall identification method [10][11][12]. The most commonly used is parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification and contact interaction control of redundant robot based on dynamic model

Li,

Bian,

Wang

2024

Phys. Scr.

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Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. The least square method is used to obtain dynamic parameters. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

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“…First, the most commonly used method for dynamic parameter identification is least squares (LS) (Kinsheel et al , 2012). Afrough and Hanieh (2019) proposed using LS to identify the dynamic parameters of an improved friction torque plus general torque model and compared the results with the dynamic parameters of a computer aided design model, obtaining promising results.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

Zhong,

Liu,

et al. 2023

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Purpose Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm. Design/methodology/approach This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification. Findings Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved. Originality/value A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

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Robust least square estimation of the CRS A465 robot arm’s dynamic model parameters

Cited by 8 publications

References 23 publications

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Parameters identification and contact interaction control of redundant robot based on dynamic model

Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

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