A distributed model for dynamic optimisation of networks

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN029124 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Subspace Algorithm for Identifying Bilinear Repetitive Processes with Deterministic Inputs

In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the “curse of dimensionality”, a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes

Deriving Mechanical Structures in Physical Coordinates from Data-Driven State-Space Realizations

In this article, the problem of deriving a physical model of a mechanical structure from an arbitrary state-space realization is addressed. As an alternative to finite element formulations, the physical parameters of a model may be directly obtained from identified parametric models. However, these methods are limited by the number of available sensors and often lead to poor predictive models. Additionally, the most efficient identification algorithms retrieve models where the physical parameters are hidden. This last difficulty is known in the literature as the inverse vibration problem. In this work, an approach to the inverse vibration problem is proposed. It is based on a similarity transformation and the requirement that every degree of freedom should contain a sensor and an actuator (full instrumented system) is relaxed to a sensor or an actuator per degree of freedom, with at least one co-located pair (partially instrumented system). The physical parameters are extracted from a state-space realization of the former system. It is shown that this system has a symmetric transfer function and this symmetry is exploited to derive a state-space realization from an identified model of the partially instrumented system. A subspace continuous-time system identification algorithm previously proposed by the authors in [1] is used to estimate this model from the IO data

Linear Parameter-Varying System Identification: New Developments and Trends (Advanced Series in Electrical and Computer Engineering - Vol. 14)

This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output LPV models Identification, set membership methods, optimization methods and subspace methods for state-space LPV models identification and orthonormal basis functions methods. Since there is a strong connection between LPV systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.https://nsuworks.nova.edu/gscis_facbooks/1022/thumbnail.jp

Introduction

The study of Linear Parameter Varying (LPV) systems was originally motivated by the control design methodology of gain scheduling [Rugh and Shamma (2000)]. The notion of LPV systems, first introduced in [Shamma and Athans (1990)], proved to be very relevant by allowing the automatic control community to overcome some limitations of the gain scheduling approach. Examples of such limitations are the large number of linear models often required to achieve a given performance, and the necessity for slow changes between two operating points. Currently, modeling, identification, and control design of LPV systems form an active research area that contributes to the development of gain scheduling controllers able to deal with fast variation of the operating points and with tight performance bounds…https://nsuworks.nova.edu/gscis_facbooks/1024/thumbnail.jp

Identification of LPV Systems with Non-White Noise Scheduling Sequences

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. Neverless, 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