Abstract
This article presents a new indirect identification method for continuous-time systems able to resolve the problem of fast sampling. To do this, a Subspace IDentification Down-Sampling (SIDDS) approach that takes into consideration the intermediate sampling instants of the input signal is proposed. This is done by partitioning the data set into m subsets, where m is the downsampling factor. Then, the discrete-time model is identified using a based subspace identification discrete-time algorithm where the data subsets are fused into a single one. Using the algebraic properties of the system, some of the parameters of the continuous-time model are directly estimated. A procedure that secures a prescribed number of zeros for the continuous-time model is used during the estimation process. The algorithm's performance is illustrated through an example of fast sampling, where its performance is compared with the direct methods implemented in Contsid.

Abstract
A new approach to gas leakage detection in high pressure distribution networks is proposed, where the pipeline is modelled as a Linear Parameter Varying (LPV) System driven by the source node mass flow with the pressure as the scheduling parameter, and the system output as the mass flow at the offtake. Using a recently proposed successive approximations LPV system subspace identification algorithm, the pipeline is thus identified from operational data. The leak is detected using a Kalman filter where the fault is treated as an augmented state. The effectiveness of this method is illustrated with an example with a mixture of real and simulated data.

Abstract
In this paper we derive a set of approximate but general bilinear Kalman filter equations for a multiinput multi-output bilinear stochastic system driven by general autocorrelated inputs. The derivation is based on a convergent Picard sequence of linear stochastic state-space subsystems. We also derive necessary and sufficient conditions for a steady-state solution to exist. Provided all the eigenvalues of a chain of structured matrices are inside the unit circle, the approximate bilinear Kalman filter equations converge to a stationary value. When the input is a zero-mean white noise process, the approximate bilinear Kalman filter equations coincide with those of the well known bilinear Kalman filter model operating under white noise inputs.

Abstract

Abstract
A novel algorithm that automatically identifies continuous-time transfer functions from Bode plots is presented. The identification is carried out in two stages. In the first one, the model order and 'good' guesses of the poles and zeros are obtained; in the second stage, estimates are refined by means of a modified Newton-Raphson algorithm. Because poles and zeros estimation only requires the magnitude curve, transport delays, if any, can be easily estimated by means of additional information supplied by the phase curve. The major and novel contribution of the proposed method resides in its first stage, where qualitative notions currently 'hidden' in the intuition of the designer are explicitly represented, yielding a simple optimization procedure that is not impaired by the presence of saddle points or local minima, and that converges very fast to the vicinity of the true solution. A detailed example is also provided to illustrate the value of the method.

Abstract
We address the identification of discrete-time linear parameter varying systems in the state-space form with affine parameter dependence. In previous work, some of the authors have addressed this problem and an iterative algorithm that avoids the curse of dimensionality, inherent to this class of problems, was developed for the identification of multiple input multiple output systems. Although convergence of this algorithm has been assured for white noise sequences, it has also converged for other type of scheduling signals. Never less, its application is still not generalized to every class of scheduling parameters. In this paper, the algorithm is modified in order to identify multiple input single output systems with quasi-stationary scheduling signals. In every iteration, the system is modeled as a linear time invariant system driven by an extended input composed by the measured input, the Kronecker product between this signal and the scheduling parameter and the Kronecker product between the scheduling and the state estimated at the previous iteration. The remaining unknown signals are considered as "noise". Furthermore, the system is decomposed into a "deterministic" system driven by the known inputs and a "stochastic" subsystem driven by noise. The system is identified as a high order autoregressive exogeneous model. In order to whiten the noise, the input/output data is filtered by the inverse noise transfer function and a state-space model is estimated for the "deterministic" subsystem. Then, the output simulated by this system is subtracted from the measurements to obtain the output stochastic component. Finally, the state of the system is estimated using a Kalman filter and a deconvolution technique. Then, the state becomes an entry to the system for the next iteration, after being multiplied by the scheduling parameter. The whole process is repeated until convergence. The algorithm is tested using periodic scheduling signals and compared with other approaches developed by the same authors. © 2012 IFAC.