Performance Improvement of Finite Time Parameter Estimation with Relaxed Persistence of Excitation Condition

In this paper, a novel finite time parameter estimation method is proposed to solve the parameter estimation problem for a class of linearly parameterized nonlinear systems. The main feature of the proposed method is that the existing method is modified via concurrent learning technique such that the strict persistence of excitation (PE) condition on the regression matrix is relaxed to a rank condition on the recorded data. This makes the presented method more practical. Furthermore, the convergence rate is improved significantly by sliding mode technique in finite time sense. The simulation results of the existing general nonlinear system illustrate the aforementioned features. Comparison with existing methods from literature proves the effectiveness of the proposed method.