Adaptive parameter identification of servo control systems with noise and high-frequency uncertainties

When identifying the parameters of practical servo systems, the high-frequency system modes are frequently neglected in order to simplify the model. Additionally, avoiding measurement noise in physical experiments is virtually impossible Therefore, selecting an appropriate excitation signal is essential. Specifying white noise, or other such signals, as the input signal may cause the excitation of high-frequency uncertainties, which affects the reliability of the parameter identification process. The current study proposes a novel approach in which a particular class of chaotic signal is employed as the excitation signal in an adaptive parameter identification process. Since chaotic signals typically have stationary, continuous, and band-limited power spectra, they are suitable for on-line parameter identification. The present numerical and experimental results demonstrate that the use of a chaotic excitation signal in the identification process causes the estimated system parameters to converge within identifiable ranges, even when the system includes measurement noise and high-frequency uncertainties. The current results also show that there is good agreement between the dynamics of the real system and those of the estimated model within the operation bandwidth.