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

Zhao, Li; Zhi, Jianhui; Yin, Ningning; Chen, Yong; Li, Jin; Liu, Jiaolong

doi:10.1007/s42835-018-00081-x

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…To develop the identification algorithm without requiring system (1) to be stable, system (10) is normalized as…”

Section: Regressor Filtering and System Normalizationmentioning

confidence: 99%

“…Refs. [10][11][12][13][14] proposed finite-time parameter estimators for robotic systems. To remove the PE condition imposed on the algorithms in [10][11][12][13][14], Refs.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13][14] proposed finite-time parameter estimators for robotic systems. To remove the PE condition imposed on the algorithms in [10][11][12][13][14], Refs. [15,16] proposed CL-based finite-time system identification algorithms for unknown systems modeled by neural networks (NNs).…”

Section: Introductionmentioning

confidence: 99%

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Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Liu,

Zhang,

2024

Electronics

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This paper proposes a novel two-stage predefined-time system identification algorithm for uncertain nonlinear systems based on concurrent learning. The main feature of the algorithm is that the convergence time of estimation error is an exact predefined parameter, which can be known and adjusted directly by users. Historic identification data are stored in the first stage to guarantee that a finite-rank condition is satisfied. In the second stage, the estimation error converges to zero for linearly parameterized uncertain systems, or it is regulated into the neighborhood of zero for unknown systems modeled by neural networks. The identification algorithm takes effect without the restrictive requirement of the persistent excitation condition. Simulation examples verify the effectiveness of the proposed method.

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“…To develop the identification algorithm without requiring system (1) to be stable, system (10) is normalized as…”

Section: Regressor Filtering and System Normalizationmentioning

confidence: 99%

“…Refs. [10][11][12][13][14] proposed finite-time parameter estimators for robotic systems. To remove the PE condition imposed on the algorithms in [10][11][12][13][14], Refs.…”

Section: Introductionmentioning

confidence: 99%

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Concurrent Learning-Based Two-Stage Predefined-Time System Identification

Liu,

Zhang,

2024

Electronics

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“…control methods have been extensively employed for discrete [7][8][9] and continuous-time [10][11][12] systems, fewer attempts have been proposed by several researchers to tackle finite-time learning such as [13][14][15][16][17][18][19]. While these results have studied finite-time learning schemes, the majority of them are concerning with continuoustime systems.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed adaptive estimation method in this work is simpler in comparison with the continuous finitetime estimation laws in [16,17], containing fractional functions, and it is able to approximate the unknown dynamics of higher-order discrete-time nonlinear systems. Contrary to [14,15,19], the proposed method does not need the inverse of the recorded regressor matrix or approximating it using an auxiliary matrix.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Discrete-time System Identification without Persistence of Excitation: Finite-time Concurrent Learning Methods

Tatari¹,

Panayiotou²,

Polycarpou³

2021

Preprint

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This paper deals with the problem of finite-time learning for unknown discrete-time nonlinear systems' dynamics, without the requirement of the persistence of excitation. A finitetime concurrent learning approach is presented to approximate the uncertainties of the discrete-time nonlinear systems in an on-line fashion by employing current data along with recorded experienced data satisfying an easy-to-check rank condition on the richness of the recorded data which is less restrictive in comparison with persistence of excitation condition. Rigorous proofs guarantee the finite-time convergence of the estimated parameters to their optimal values based on a discrete-time Lyapunov analysis. Compared with the existing work in the literature, simulation results illustrate that the proposed method can timely and precisely approximate the uncertainties.

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