Abstract
The difficulty of adding external excitation and the asynchronous data collection from the industrial robot operation limited the online parameter identification of industrial robots. In this regard, this study proposes an identification method that only uses the amplitude of the frequency response function (FRF) of the system to identify robot joint torsional stiffness and dynamic parameters. The error criterion function shows that this method is feasible and comparable to applying the complete frequency response for identification. The Levenberg–Marquardt (L-M) algorithm is used to find the global optimal value of the error criterion function. In addition, an operational excitation method is proposed to excite the system. The speed profile is set as a triangle wave to excite the system using rectangular wave electromagnetic torques. The simulation results show that using the amplitude of the FRF to identify parameters applies to asynchronous data. The experiments on a single-degree-of-freedom articulated arm test bench show that the motion excitation method is effective, and both stiffness and inertia are identifiable.
Highlights
Servo systems have become wildly used with the development of industry and technology, in high-end medical equipment, new energy, robots, CNC machine tools, and other fields [1,2]
The results showed that the Least Square (LS) method and Adaptive Linear Neuron (Adaline) method have the advantages of less iterations, short calculation time, and high accuracy
The typical industrial robot joint is composed of a permanent magnet synchronous motor (PMSM), a
Summary
Servo systems have become wildly used with the development of industry and technology, in high-end medical equipment, new energy, robots, CNC machine tools, and other fields [1,2]. For the dual inertia system with nonlinear characteristics, Beineke [12] proposed two identification methods for external mechanical parameters: standard minimum root mean square and four-step instrumental variable method. They extracted the speed response of the motor at different stages for the dual inertia system with nonlinear characteristics and identified the parameters corresponding to each stage. The traditional method depends on the external excitation signal, which is difficult to implement in the operation of an industrial robot. In order to evaluate the rigidity of the joint servo system of industrial robots in operation, the identification of the system shaft stiffness under the condition of operational excitation and asynchronous sampling of input and output is studied. This research provides a valuable reference for condition monitoring of the joint in actual industrial applications
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