Third-order cumulants based methods for continuous-time errors-in-variables model identification
Automatica (Journal of IFAC)
Long-term fading channel estimation from sample covariances
Automatica (Journal of IFAC)
Identification of linear dynamic systems operating in a networked environment
Automatica (Journal of IFAC)
Brief paper: Issues in sampling and estimating continuous-time models with stochastic disturbances
Automatica (Journal of IFAC)
Refined instrumental variable methods for identification of LPV Box-Jenkins models
Automatica (Journal of IFAC)
Nonlinear adaptive control of a chemical reactor
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
Automatica (Journal of IFAC)
Analytical identification of discrete objects
Automation and Remote Control
Adaptive pattern classification for symbolic dynamic systems
Signal Processing
International Journal of Systems, Control and Communications
Parameter and differentiation order estimation in fractional models
Automatica (Journal of IFAC)
Mathematical model for robust control of an irrigation main canal pool
Environmental Modelling & Software
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System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance. Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay. The CONtinuous-Time System IDentification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.