Orthogonal Functions may be divided into two classes. The class of continuous systems and the discontinuous class of piecewise constant systems. Problems arise because continuous systems form an unsatisfactory basis for the expansion of functions containing discontinuities whilst piecewise constant systems insert artificial discontinuities into all representations. Since these two classes of functions would be unsuccessful in coping with functions that possess both continuity and discontinuity we must look to General Hybrid Orthogonal Functions (GHOF) which have been shown to be the most appropriate in such situations. This book introduces the system of GHOF, discusses its properties, develops an operational algebra for the discretization of continuous dynamic systems on the system of GHOF and illustrates its use as a flexible and powerful framework of computational tools in a wide range of systems and control.

Models of dynamical systems are required for various purposes in the field of systems and control. The models are handled either in discrete time (DT) or in continuous time (CT). Physical systems give rise to models only in CT because they are based on physical laws which are invariably in CT. In system identification, indirect methods provide DT models which are then converted into CT. Methods of directly identifying CT models are preferred to the indirect methods for various reasons. The direct methods involve a primary stage of signal processing, followed by a secondary stage of parameter estimation. In the primary stage, the measured signals are processed by a general linear dynamic operation-computational or realized through prefilters, to preserve the system parameters in their native CT form-and the literature is rich on this aspect. In this book: Identification of Continuous-Time Systems-Linear and Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti Prasada Rao consider CT system models that are linear in their unknown parameters and propose robust methods of estimation. This book complements the existing literature on the identification of CT systems by enhancing the secondary stage through linear and robust estimation. In this book, the authors provide an overview of CT system identification, consider Markov-parameter models and time-moment models as simple linear-in-parameters models for CT system identification, bring them into mainstream model parameterization via basis functions, present a methodology to robustify the recursive least squares algorithm for parameter estimation of linear regression models, suggest a simple off-line error quantification scheme to show that it is possible to quantify error even in the absence of informative priors, and indicate some directions for further research. This modest volume is intended to be a useful addition to the literature on identifying CT systems.