Ganti Prasada Rao

Bio: Ganti Prasada Rao is an academic researcher. The author has contributed to research in topic(s): Orthogonal functions & Piecewise. The author has an hindex of 1, co-authored 1 publication(s) receiving 183 citation(s).

Piecewise Constant Orthogonal Functions and Their Application to Systems and Control

Abstract: I Piecewise constant orthogonal basis functions.- II Operations on square integrable functions in terms of PCBF spectra.- III Analysis of lumped continuous linear systems.- IV Analysis of time delay systems.- V Solution of functional differential equations.- VI Analysis of non-linear and time-varying systems.- VII Optimal control of linear lag-free systems.- VIII Optimal control of time-lag systems.- IX Solution of partial differential equations (PDE) [W55].- X Identification of continuous lumped parameter systems.- XI Parameter identification in distributed systems.

Identification of continuous-time systems

Abstract: System identification is a well-established field. It is concerned with the determination of particular models for systems that are intended for a certain purpose such as control. Although dynamical systems encountered in the physical world are native to the continuous-time domain, system identification has been based largely on discrete-time models for a long time in the past, ignoring certain merits of the native continuous-time models. Continuous-time-model-based system identification techniques were initiated in the middle of the last century, but were overshadowed by the overwhelming developments in discrete-time methods for some time. This was due mainly to the 'go completely digital' trend that was spurred by parallel developments in digital computers. The field of identification has now matured and several of the methods are now incorporated in the continuous time system identification (CONTSID) toolbox for use with Matlab. The paper presents a perspective of these techniques in a unified framework.

A review of identification in continuous-time systems

Abstract: This paper aims at taking the reader on a guided tour of the field of identification of continuous-time systems. It presents a birds eye view of the continuous-time related aspects of the greater field of system identification. Continuous-time based contributions to system identification began in the nineteen-fifties but were overshadowed by a ‘go completely digital’ spirit which was spurred by parallel developments in digital computers during the following two decades. The nineteen seventies have witnessed a resurgence of continuous-time spirit and the field of continuous-time system identification has now matured to merit a review as is intended here. This paper is divided into three parts. An overview of the basic techniques of identification of continuous-time systems in a unified framework is presented in Part A. Parts B and C outline some recent developments in the identification of linear systems and nonlinear systems, respectively.

Numerical solution of nonlinear Fredholm integral equations of the second kind using Haar wavelets

Abstract: In this work, we present a computational method for solving nonlinear Fredholm integral equations of the second kind which is based on the use of Haar wavelets. Error analysis is worked out that shows efficiency of the method. Finally, we also give some numerical examples.

Numerical solution of integral equations system of the second kind by block-pulse functions

Abstract: This paper endeavors to formulate Block–Pulse functions to propose solutions for the Fredholm integral equations system. To begin with we describe the characteristic of Block–Pulse functions and will go on to indicate that through this method a system of Fredholm integral equations can be reduced to an algebraic equation. Numerical examples presented to illustrate the accuracy of the method.

Numerical solution of stochastic Volterra integral equations by a stochastic operational matrix based on block pulse functions

Abstract: This article proposes an efficient method for solving stochastic Volterra integral equations. By using block pulse functions and their stochastic operational matrix of integration, a stochastic Volterra integral equation can be reduced to a linear lower triangular system, which can be directly solved by forward substitution. The results show that the approximate solutions have a good degree of accuracy.