HIGHLIGHTS
- who: Ye Li et al. from the School of Information Science and Engineering, Northeastern University, Shenyang, China have published the Article: RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer, in the Journal: Computational Intelligence and Neuroscience of 22/08/2022
- what: In this paper an adaptive parameter identification method based on an improved sliding mode observer is proposed. In , an improved SMO load torque adaptive identification method is proposed, which is applied to the motor running under variable operating conditions, and the load has been fluctuating. The main . . .
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