An integral method for parameter identification of a nonlinear robot subject to quantization error

Yang Rui Li, Chao Chung Peng, Jer Nan Juang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper focuses on the parameter identification of a class of nonlinear mechanical dynamic systems subject to feedback quantization errors. A second-order integral method for the parameter identification of a nonlinear two-degrees-of-freedom robotic manipulator is studied. To obtain accurate parameter estimates, an optimal excitation trajectory subject to state constraints is proposed. The designated excitation trajectory is able to minimize estimation uncertainties, providing the controlled system trajectory reaches the desired optimal excitation trajectory. Moreover, for most of the nonlinear robotic systems, the output measurements are acquired from the encoders subject to apparent quantization error. Directly differentiating the position to estimate the velocity and acceleration trajectories is unrealistic and unsuitable due to the amplified quantization errors will cause considerable estimation errors. Instead, the finite Fourier series is introduced to perform optimal estimation of the position, velocity, and acceleration histories to capture the dominant frequencies of the system response. A weighted least-squares solution is further proposed to improve parameter identification precision. The comparative studies reveal that the integral method has superior parameter estimation performance than the direct difference model and the filtered regression model in the existing literature. Experimental simulations illustrate the effectiveness of the presented method.

Original languageEnglish
Pages (from-to)22419-22441
Number of pages23
JournalNonlinear Dynamics
Volume111
Issue number24
DOIs
Publication statusPublished - 2023 Dec

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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