Simultaneous generation of sensitivity functions - Transfer function matrix approach

Abstract Sensitivity functions are used in many model parameter identification methods and adaptive control algorithms. Since one sensitivity model has to be used for each parameter, the computation requirement is large when many parameters have to be identified or adjusted. This paper proposes a method to reduce the computation requirement by using one sensitivity model suitable for the generation of sensitivity functions for all parameters. Instead of using modal transformations and eigenvector sensitivities as previously proposed, this paper uses an input-output transfer function matrix approach. Several sufficient conditions in terms of controllability are given for the implementation of the method. An estimate of computation savings is provided.