In this paper, we extends the innovation vector to the innovation matrices and presents a filtering based multi-innovation extended stochastic gradient algorithm for multi-input multi-output controlled autoregressive moving average systems. The basic idea is using the filtering technique to transform a multivariable system into two identification models, then to identify the parameters of these two identification models interactively. The proposed multi-innovation identification algorithm can effectively improve the parameter estimation accuracy.