Momentum‐innovation recursive least squares identification algorithm for a servo turntable system based on the output error model

In this article, the parameter identification problem of the output error model is investigated under the application requirement of online parameter identification of a two‐degree‐of‐freedom servo turntable system. To eliminate the influence of colored noise in the observed output signal of the output error model on the algorithm identification accuracy, the momentum factor is introduced into the innovation term of the recursive least squares algorithm, and the momentum‐innovation recursive least squares (MI‐RLS) algorithm is proposed. Further, to improve the convergence speed and identification accuracy of the algorithm while avoiding increasing the algorithm complexity significantly, a reframed multi‐innovation strategy is introduced, and a momentum reframed multi‐innovation least squares (MR‐MILS) algorithm is developed. After analyzing the complexity of the proposed algorithms, the convergence performances of the two algorithms are verified using the theory of martingale convergence, and the results show that the reframed multi‐innovation strategy can accelerate the convergence speed of the MR‐MILS algorithm. The effectiveness of the proposed identification approach is demonstrated via simulation results.