This article proposes a tracking controller for servomechanisms with a continuous friction model. The parameters of this model are estimated through a proposed two-step offline identification methodology that uses a linear extended state observer (LESO) based on the integral of position measurements, termed as ILESO. In the first step, the viscous and Coulomb coefficients of this friction model are estimated by the ordinary least squares method. The second step employs these estimates to obtain the initial guess for the nonlinear least squares method that identifies all the parameters of the nonlinearly parameterized friction model. Finally, a backstepping controller is designed for reference tracking, which uses the estimated offline friction model and online estimations of the servo position, velocity, and system uncertainty. These online estimations are provided by a modified ILESO, termed as FILESO, that has a similar structure to that of the ILESO but incorporates the identified offline friction model into its dynamics. The convergence and stability of the proposed observer and controller are established using contraction analysis. Experimental results show that the proposed approach exhibits satisfactory performance.

This paper proposes a method that estimates the parameters of a velocity-controlled servo. A proportional-integral controller, which uses only position measurements, closes the loop. The proposed approach uses the steady-state response produced by steps and sine-wave signals; they do not produce high levels of vibration on the servo compared with random signals commonly used with the least squares algorithm; moreover, it relies on simple numerical calculations. The method, which is called in the sequel as the steady-state response method (SSRM), consists of two steps. The first step uses three constant reference inputs in order to identify a constant disturbance and the viscous and Coulomb friction coefficients of the servo. In the second step, the SSRM estimates the servo inertia using a sine wave plus a constant signal as a velocity reference input and employs the estimate of the viscous friction coefficient obtained in the first step. Experiments on a testbed employing a brushless servomotor allow comparing the results obtained using the SSRM and those produced by a standard recursive least squares method (RLSM).

This study proposes a two-step identification method for estimating the four parameters of a nonlinear model of a position-controlled servomechanism. In the first step, the proposed approach, called the algebraic recursive identification method (ARIM), uses a parametrization derived from the operational calculus currently employed in algebraic identification methods (AIM) recently proposed in the literature. The procedure for obtaining this parametrization eliminates the effect of constant disturbances affecting the servomechanism and filters out the high-frequency measurement noise. A recursive least squares algorithm uses the parametrization for estimating the linear part of the servomechanism model, and allows eliminating the singularity problems found in the AIM. The second step uses the parameters obtained in the first step for computing the Coulomb friction coefficient and the constant disturbance acting on the servomechanism. Experimental results on a laboratory prototype allow comparing the results obtained using the AIM and the ARIM.

A novel on-line system identification method for shear beam building models, based on a wave propagation approach, is developed as an alternative solution to modal analysis methods for the health assessment of multi-story buildings. A discrete shear beam model is introduced that is used to design an adaptive observer, allowing for the estimation of displacements and velocities, as well as the unknown shear wave velocities and damping coefficients in real-time. The adaptive observer design is based only on acceleration measurements, does not need a coordinate transformation, and uses the normalized recursive least squares method with forgetting factor and a parameter projection scheme to achieve stronger convergence. Moreover, the proposed identification scheme employs a novel parameterization based on linear integral filters, which eliminates constant disturbances and attenuates measurement noise. The algorithm efficiency is demonstrated through experimental results on a reduced scale five-story building.