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The least-squares method is one of the most efficient and simple identification methods commonly used. Unfortunately, it is very sensitive to large errors (outliers) in the input/output data. In such cases, it may never converge or give erroneous results. In earlier work, based on the minimax principle, a robust least-squares version has been proposed, but it is only suitable for linear systems. In practice, most real systems are nonlinear. Many of these can be suitably represented by bilinear models. In the paper, a robust recursive least-squares method has been proposed for bilinear system identification. It differs from earlier approaches in that it uses modified weights in the criterion for robustness. A theorem proving the convergence of the proposed algorithm is included. Results of the simulation demonstrating the robustness of the proposed algorithm are also included.

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