Convergence analysis of an online recursive identification method with uncomplete communication constraints

Under the network environment, the uncomplete infromation causes an undesirable effect on the parameter identification and convergence. Unlike the traditional recursive least squares (RLS) algorithm, the paper proposes a novel online recursive identification method with uncomplete communication constraints. In this algorithm, the Bernoulli process is firstly employed to describe the character of data packet losses, and the uncomplete information is compensated by the auxiliary model strategy. The new data information matrix is then constructed, which is decomposed by QR decomposition and the intermediate matrix can be updated recursively. An new recursive least squares (RLS) algorithm under networks with random packet losses is then presented, and its convergence is analysed. Simulation confirms the feasibility and efficiency of the proposed method. Copyright © 2015 Acta Autornatica. All rights reserved.