A recursive batch least square (LS) identification algorithm with constant observation matrix dimension

A batch least square (LS) identification algorithm with recursive formulation is proposed to deal with the problems occurred when the ordinary to algorithm is applied to real-time and online identification. The fundamental feature of this algorithm is to fix the observation matrix´s dimension, making use of a definite amount of newly obtained data and ignoring the older data. Taking advantage of the recursive formulation, this algorithm avoids the inverse operation and thus reduces the calculation, making itself capable of application in real-time and online identification case. Simulation shows that this algorithm has rather good precision and tracking ability, which are greatly influenced by the dimension of the observation matrix