A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems
For large-scale systems, this provides a distributed approach to module identification with theoretical guarantees, though it is an incremental extension of existing RLS methods.
The paper presents a distributed identification method for MISO FIR systems using recursive least squares estimators with limited information exchange, achieving asymptotic convergence to true parameters and unbiasedness under disturbances.
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.