Unconstrained Parametrizations of Discrete-Time Linear Input-Output Models: Stability and Dissipativity by Construction

It is often required that identified models exhibit certain stability and dissipativity properties, e.g., passivity or ℓ ₂. The aim of this paper is to develop an unconstrained parametrization of linear parameter-varying (LPV) input-output (IO) discrete-time (DT) models that guarantees stability/dissipativity by construction, i.e., the model is stable/dissipative for any choice of model parameters. To achieve this, it is shown that any quadratically stable/dissipative DT-LPV-IO model can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball. The unit ball is reparameterized through a Cayley transformation, resulting in a fully unconstrained parameterization. These results immediately apply to linear time-varying IO models. In the linear time-invariant case, an unconstrained parameterization of all stable/dissipative DT transfer functions is obtained. The unconstrained parametrization enables, among others, the use of neural network coefficient functions in LPV system identification while guaranteeing stability and dissipativity.