Identification of multivariable continuous-time systems

This chapter presents a parameter identification algorithm for multivariable, continuous-time systems. It is shown how to treat systems with (non-zero) initial conditions and measurement offset or bias. The system model structure used is a minimal order input-output representation. Differentiation of measured data is avoided by means of either multiple lowpass filtering or multiple finite time integration. Equations in unknown parameters are set up and then solved by means of the linear least-squares. The singular value decomposition has been used to solve the least-squares problem because of its numerical robustness and because of the extra data it provides for analysis. Examples of up to 14th order are discussed.