A deep motion reliability scheme for robotic operations. (May 2023)

• Record Type: Journal Article
• Authors: Bao, Dan; Liang, Xiaoling; Ge, Shuzhi Sam; Hou, Baolin
• Is Part Of: Mechanism and machine theory. Volume 183(2023)
• Publication Date: 2023-05
• Subjects: Motion reliability -- Robot manipulator -- Adaptive control -- NARX -- Interval number; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2023.105280
• Languages: English
• ISSNs: 0094-114X

Abstract

Abstract: This paper proposes a motion reliability analysis method for robotic operations considering uncertainties and external disturbances in the system. First, the kinematic and dynamic model of the robot is established, and the interval number is adopted to describe the uncertain parameters. Then, an observed-based neural adaptive control scheme is developed to guarantee uniform ultimate boundedness of all the signals in the closed loop. Furthermore, a motion reliability model is presented based on interval set operation with the time series of tracking error boundaries. In addition, to improve the computational efficiency, a non-linear autoregressive network with exogenous inputs (NARX) and a segmented sampling technique are proposed to model the dynamic response of the system. Simulation results demonstrate that the proposed analysis method has high accuracy and efficiency in assessing motion reliability. Highlights: Proposes a deep motion reliability analysis method for robotic operations. Develops an observer-based neural adaptive control method. Considering the coupled effect of structural uncertainties and controller characteristics. Introduces a time-series dynamic network and a segmented sampling technique.