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A novel recursive predictor-based subspace identification method is presented to identify linear time-invariant systems with multi inputs and multi outputs. The method is implemented in real-time and is able to operate in open loop or closed loop. The recursive identification is performed via the subsequent solution of only three linear problems, which are solved using recursive least squares. The recursive implementation of the method is not only able to identify linear time-invariant models from measured data, but can also be used to track slowly time-varying dynamics if adaptive filters are used. The computational complexity is reduced by exploiting the structure in the data equations and by using array algorithms to solve the main linear problem. This results in a fast recursive predictor-based subspace identification method suited for real-time implementation. The real-time implementation and the ability to work with multi-input and multi-output systems operating in closed loop makes this approach suitable for online estimation of unstable dynamics. The ability to do so is demonstrated by the detection of flutter on an experimental 2-D-airfoil system.