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A model reference technique is used to develop adaptive identification schemes for multivariable continuous linear dynamical systems which contain unknown parameters linearly. In this paper, the conditions which assure the identifiability of unknown process parameters are obtained and four types of identification schemes are proposed. It is proved by the Liapunov's direct method that the estimation errors of these types are asymptotically stable in the large when the measurement noise is free. This tequnique is also extended so that it can apply to a sampled data system and consequently, the technique becomes available in real time estimation using a digital computer. As an application of this adaptive identification approach, the physical parameters of a parallel heat exchanger are identified using a fourth order approximate model obtained by the Method of Weighted Residuals. The results show that this approach is sufficiently useful to the parameter identification of a distributed parameter system.

Adaptive Identification of Multivariable Linear Dynamical Systems Containing Parameters Linearly