A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems

TL;DR

This paper introduces a recursive, distributed method for identifying large-scale MISO FIR systems, enabling local parameter estimation with limited communication and proven convergence properties.

Contribution

It presents a novel recursive estimation approach for distributed identification of MISO FIR systems, with convergence guarantees and minimal information exchange.

Findings

Estimators converge asymptotically to true parameters without disturbances.

Limited information exchange suffices for accurate local estimation.

Method applicable to large-scale systems with multiple inputs.

Abstract

The problem of identifying single modules in multiple-input-single-output (MISO) systems is considered. A novel approach to distributed identification of MISO finite impulse response systems is presented. The distributed identification is discerned by the local estimation of local parameters, which correspond to a module in the MISO system. The local estimators are derived from the standard recursive least squares estimator and require limited information exchange. By Lyapunov's second method, sufficient conditions are derived for asymptotic convergence of the estimators to the true parameters in the absence of disturbances, which lead to asymptotic unbiasedness in the presence of additive output disturbances.