Abstract
This paper deals with the parameter identification problem for nonlinear mechanical systems based on state estimation. Here, the concept of Sliding Mode Observer for finite time state estimation and the Least-Square Method for parameter identification have been combined; thus, guaranteeing that the estimated state converges to the real one in a finite time. The asymptotic parameter identification is performed by applying the Least-Square approach, minimizing the so-called joint uncertainty; in this process, a specific persistent excitation condition is introduced to guarantee the effectiveness of the proposed identification algorithm. With the proposed approach and some considerations, the algorithm is capable of estimating friction coefficients and inertia moments, within a narrow time-window. Finally, the performance of the identification algorithm designed in this paper is tested on a real-time underactuated system, specifically the double pendulum on a cart platform. Furthermore, a successful benchmarking between the algorithm herein and the traditional least-square method is reported.
