Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning

Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots...

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Bibliographic Details
Published in:Science China. Technological sciences 2022-02, Vol.65 (2), p.386-395
Main Authors: Shen, Tan, Dong, YunLong, He, DingXin, Yuan, Ye
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Distance learning
Dynamic stability
Dynamical systems
Engineering
Identification methods
Industrial robots
Machine learning
Mathematical analysis
Real time
Recursive methods
Robot arms
Robot dynamics
Robots
System identification
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Summary:Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning (SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted ℓ 1 -minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm (SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots.
ISSN:1674-7321
1869-1900
DOI:10.1007/s11431-021-1947-5