LAUSR

In the identification of industrial robot joint system, in order to obtain the ideal frequency response of the system, additional excitation needs to be applied. The addition of excitation signals, such as chirp signals and pseudorandom binary signals, will make the instantaneous angular speed (IAS) obtained by the motor encoder present nonstationary characteristics. In order to solve this problem, this article proposes a method for system identification by IAS extraction under nonstationary conditions. Interferences, such as electrical perturbation, quantization error, and background noise, generally reduce the accuracy of extracted IAS. First, the influence of electrical perturbation on the accuracy of angle calculation is eliminated by utilizing the characteristics of the two-phase encoder signals. Second, considering the time-varying characteristics of the rotational speed in the identification process, a nonuniform local polynomial differentiator (NULPD) is proposed to optimize the estimation of IAS and reduce the influence of background noise and sampling error based on local polynomial differentiator (LPD). The IAS estimation under the condition of variable speed is simulated to verify the performance of NULPD. According to the transmission characteristics of the robot joint system, a two-mass dynamic model is established, and the transfer function between the torque and motor speed is obtained. The torque is calculated from the three-phase current signals, and the rotational speed is obtained by the encoder signal. The frequency sweep experiment is carried out on the single-degree-of-freedom articulated arm test bench, where the stiffness and inertia of the system are identified within a certain error range by the proposed method.