Parameters Recursive Identification for Minimum Variance Control

In this paper, we discuss the problem of parameters recursive identification and designing an optimal input signal for minimum variance control from the point of system identification. Consider the unknown parameter vector of the ARMAX model in the minimum variance closed loop control, we propose multi-innovation recursive least-squares identification method and separable iterative recursive least-squares identification method to identify and estimate the unknown parameters vector in the ARMAX model on line. When excited by the white noise, the two identification methods will give the unbiased estimation about the unknown parameter vector. When excited by the color noise, only the separable iterative recursive least-squares identification method can give the unbiased estimation. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.