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June 12, 2026Optimal Control Applications and Methods0 citations

Momentum Stochastic Gradient Algorithm With Data Filtering for Generalized Time‐Varying Systems

KYKai YaoQingdao University of Science and Technology YJYan JiQingdao University of Science and Technology HFHao FangWuhan Donghu University

Key Points

The research aims to improve parameter estimation for generalized time-varying systems using an accelerated gradient method.
Proposed a momentum-based parameter adaptation law to enhance convergence speed and reduce oscillations.
Applied a data filtering technique to preprocess input and output signals.
Validated the algorithm using a DC motor system.
Achieved accelerated convergence in the parameter estimation process.
Effectively reduced the influence of colored noise on parameter estimation.
Demonstrated significant improvements in performance metrics of the DC motor system.

Abstract

ABSTRACT This article proposes an accelerated gradient‐based parameter estimation for generalized time‐varying systems. A momentum term is incorporated into the parameter adaptation law to accelerate convergence and suppress oscillatory behavior. In addition, the data filtering technique is applied to preprocess the input and output signals, effectively attenuating the influence of colored noise on parameter estimation. The proposed algorithm is further validated through its application to a DC motor system.

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