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  • Recursive least squares with domain-driven variable-direction forgetting [Elektronski vir]
    Černe, Gregor, 1991- ; Škrjanc, Igor
    Recursive least squares (RLS) algorithm assumes persistent excitation; but this condition is rarely fulfilled in closed-loop systems, where control performance has higher priority over persistent ... excitation. Variable-directional forgetting (VDF) partly mitigates the problem by confining forgetting to a low-dimensional subspace, thereby averting parameter divergence when the excitation is not persistent. Nevertheless, in this paper we argue that the VDF is sub-optimal: (i) it may forget the entire covariance matrix under rank-deficient excitation, and (ii) it introduces an additional tuning parameter that complicates system design.To overcome these drawbacks, a recursive least squares with domain-driven directional forgetting (RLS-3DF) is developed. Instead of minimizing the squared error over discrete samples, RLS-3DF minimizes the expected squared error over an operating region of the input domain, a region where the user wants the model to be accurate. This continuous-domain formulation, inspired by the separation of validity functions and local parameters in Takagi-Sugeno fuzzy models, decouples the influence of sampling distribution from the accuracy requirement. The resulting algorithm retains information along non-excited directions and does not introduce any additional hyper-parameters.The results look promising, as RLS-3DF improves RLS-VDF across the board by at least 20%, even under conditions that satisfy persistent excitation, which is the most surprising and encouraging result.
    Source: FUZZ-IEEE [Elektronski vir] : Reims (Fr.), July 6-9, 2025 : 2025 IEEE International Conference on Fuzzy Systems : conference proceedings ([6] str.)
    Type of material - conference contribution ; adult, serious
    Publish date - 2025
    Language - english
    COBISS.SI-ID - 249763843

    Link(s):

    https://ieeexplore.ieee.org/document/11152228
    DOI

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