Indirect Continuous-Time LPV System Identification Through a Downsampled Subspace Approach
Linear Parameter-Varying System Identification, New Developments and Trends (Advanced Series in Electrical and Computer Engineering)
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.
World Scientific Publishing
Lopes dos Santos, Paulo; Azevedo Perdicoulis, Teresa Paula; Ramos, Jose A.; and Martins de Carvalho, Jorge L., "Indirect Continuous-Time LPV System Identification Through a Downsampled Subspace Approach" (2011). CCE Faculty Books and Book Chapters. 29.