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<p>The sinusoidal signal parameter estimation problem is studied in this paper, where prescribed-time estimators with finite time-varying gains are proposed. The advantage of the proposed estimators is that the convergence time can be prescribed by the user, independent of the initial conditions. Since the estimation algorithms are derived in a continuous-time form, which cannot be directly applied to digital control systems, the practical applicability of the algorithms in embedded systems in a discrete-time form is particularly investigated. A forward-Euler method is employed, and a hardware platform is established to realize the prescribed-time algorithms in embedded systems. Several key factors in the discretization are analyzed, including the system noises, clock ratios, and the Euler-discretization steps. Empirical formulas for determining the switching time of the algorithm and the minimum prescribed convergence time in practice are derived. Extensive numerical simulations and experiments are conducted to demonstrate the effectiveness of the theoretical algorithms as well as the empirical formulas.</p>

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Prescribed-time sinusoidal signal parameter estimation: continuous-time design with discrete-time implementation for embedded systems

  • 期刊论文

  • 发表日期：

    2025年6月21日

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  • Yinlong Hu,
  • Changjun Cheng,
  • Xuhao Cai,
  • Jiawei Peng,
  • Shang Shi

摘要

The sinusoidal signal parameter estimation problem is studied in this paper, where prescribed-time estimators with finite time-varying gains are proposed. The advantage of the proposed estimators is that the convergence time can be prescribed by the user, independent of the initial conditions. Since the estimation algorithms are derived in a continuous-time form, which cannot be directly applied to digital control systems, the practical applicability of the algorithms in embedded systems in a discrete-time form is particularly investigated. A forward-Euler method is employed, and a hardware platform is established to realize the prescribed-time algorithms in embedded systems. Several key factors in the discretization are analyzed, including the system noises, clock ratios, and the Euler-discretization steps. Empirical formulas for determining the switching time of the algorithm and the minimum prescribed convergence time in practice are derived. Extensive numerical simulations and experiments are conducted to demonstrate the effectiveness of the theoretical algorithms as well as the empirical formulas.