Abstract (EN):
The successive approximation Linear Parameter Varying systems subspace identification algorithm for discrete-time systems is based on a convergent
sequence of linear time invariant deterministic stochastic state-space approximations. In this chapter, this method is modified to cope
with continuous-time LPV state-space models. To do this, the LPV system is discretised, the discrete-time model is identified by the successive
approximations algorithm and then converted to a continuous-time model. Since affine dependence is preserved only for fast sampling, a
subspace downsampling approach is used to estimate the linear time invariant deterministic-stochastic state-space approximations. A second order simulation example, with complex poles, illustrates the effectiveness of the new algorithm.
Language:
English
Type (Professor's evaluation):
Scientific