Basic algorithms for multivariable system identification by subspace techniques are briefly described. Deterministic and combined deterministic stochastic identification problems are dealt with using two approaches. A state space model is computed from input-output data sequences. Multiple data sequences, collected by possibly independent identification experiments, can be handled. Sequential processing of large data sets is provided as an option. Illustrative numerical examples are included.
control system design; identification methods: invariant subspaces; least squares solutions; multi-variable systems, numerical linear algebra; QR factorization; singular value decomposition.
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