Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Tang, Minan; Yan, Yaguang; An, Bo; Wang, Wenjuan; Zhang, Yaqi

doi:10.3390/machines11020316

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…At the same time, these values of the parameters underlying the robot promote the design of new control methods [5]. Tang et al proposed an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO), which helps to improve the accuracy and stability of trajectory control of collaborative robots [6]. Dong et al proposed an online parametric estimation algorithm under the framework of the least squares algorithm and analyzed the convergence of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Liu,

Zhang,

Wei

et al. 2024

Actuators

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Aiming to solve the problem that the significant error between the actual joint torque and the calculated joint torque of a welding robot leads to the vibration of the end-effector, which in turn affects the stability of the end-effector, this paper proposes a identification algorithm based on the Weighted Least Squares Genetic Algorithm (WLS-GA) to construct and solve the dynamical model to obtain the accurate dynamical parameters. Firstly, a linear model of welding robot dynamics is derived. The fifth-order optimal Fourier series excitation trajectory is designed to collect experimental data such as joint torque. Then, a rough solution of the parameters to be recognized is obtained by solving the dynamics model through the Weighted Least Squares (WLS) method, the search space is determined based on the rough solution, and the optimal solution is obtained by using the Genetic Algorithm (GA) to perform a quadratic search in the search space. Finally, the identification data obtained from the algorithm is analyzed and compared with the experimental data. The results show that the error between the identification data obtained using the WLS-GA identification algorithm and the experimental data is relatively small. The results show that the identification data obtained using the WLS-GA identification algorithm have less error than the experimental data, taking the Root Mean Square (RMS) value of the joint torque error obtained using the weighted least squares algorithm as a criterion. The accuracy of the WLS-GA identification algorithm can be improved by up to 66.85% compared with that of the weighted least squares algorithm and by up to 78.0% compared with that of the Ordinary Least Squares (OLS) algorithm. In summary, the WLS-GA identification algorithm can accurately identify the dynamic parameters of the welding robot and more accurately construct a dynamic model to solve the effect of joint torque error on the control characteristics of the welding robot. It can improve the stability of the end-effector of the welding robot to ensure the quality of the automobile body and beam welding and welding speed.

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Section: Introductionmentioning

confidence: 99%

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Liu,

Zhang,

Wei

et al. 2024

Actuators

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show abstract

“…Tadese et al (2022) used weighted least squares regression for dynamic parameter identification. Tang et al (2023) proposed a recognition algorithm based on weighted least squares and random weighted particle swarm optimization. Huang et al (2022) proposed an iterative hybrid least square algorithm for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Research on dynamic parameter identification and collision detection method for cooperative robots

Sun,

Song,

Wang

et al. 2023

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Purpose The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots. Design/methodology/approach This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans. Findings Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation. Originality/value This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.

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Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Lu,

Wei,

et al. 2024

Soft Comput

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Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Cited by 8 publications

References 29 publications

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Identification Algorithm for Stability Improvement of Welding Robot End-Effector

Research on dynamic parameter identification and collision detection method for cooperative robots

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

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