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Showing papers on "Feedback linearization published in 1979"


Journal ArticleDOI
02 Apr 1979
TL;DR: In this paper, the problem of designing a feedback system with prescribed properties is attacked via a fractional representation approach to feedback system analysis and synthesis, and the theory is formulated axiomatically to permit its application in a wide variety of system design problems and is extremely elementary in nature requiring no more than addition, multiplication, subtraction and inversion for its derivation even in the most general settings.
Abstract: The problem of designing a feedback system with prescribed properties is attacked via a fractional representation approach to feedback system analysis and synthesis. To this end we let H denote a ring of operators with the prescribed properties and model a given plant as the ratio of two operators in H . This, in turn, leads to a simplified test to determine whether or not a feedback system in which that plant is embedded has the prescribed properties and a complete characterization of those compensators which will "place" the feedback system in H . The theory is formulated axiomatically to permit its application in a wide variety of system design problems and is extremely elementary in nature requiring no more than addition, multiplication, subtraction, and inversion for its derivation even in the most general settings.

626 citations


Journal ArticleDOI
01 Jun 1979
TL;DR: In this paper, a new version of the method of inequalities is developed for the design of feedback control systems that have nonlinear and multivariable characteristics and are subject to disturbances.
Abstract: A new version of the method of inequalities is developed for the design of feedback control systems that have nonlinear and multivariable characteristics and are subject to disturbances. The system is required to behave in accordance with a criterion which takes the form of a prescribed bound on each component of the error vector.

84 citations


Journal ArticleDOI
TL;DR: This paper presents a method of formal linearization by augmenting state variables for nonlinear systems by introducing a sequence of linearly independent functions and regarding each of them as a new state variable.
Abstract: This paper presents a method of formal linearization by augmenting state variables for nonlinear systems. Introducing a sequence of linearly independent functions and regarding each of them as a new state variable, the original nonlinear system is transformed into an augmented linear system. Based on this linearization, the paper also derives an observer and a suboptimal controller for nonlinear systems. The examples indicate that the linearization has excellent characteristics and greatly improves the performances.

39 citations






Journal ArticleDOI
TL;DR: In this paper, the concept of error as applied to feedback systems is considered and error constants and coefficients are developed for application to systems with non-unity feedback for non-uniform scalar feedback.
Abstract: The concept of error as applied to feedback systems is considered Error constants and coefficients are developed for application to systems with non-unity feedback Non-unity scalar feedback factors are considered as well as feedback functions of s


Journal ArticleDOI
TL;DR: In this article, the authors developed and evaluated approximate analyticalsolutions of nonlinear differential equations that purport to model physical ecological systems of a predator-prey nature, and two techniques of solution are presented and applied to two specific examples.
Abstract: The purpose of this paper is to develop and evaluate approximate analyticalsolutions of nonlinear differential equations that purport to model physical ecological systems of a predator-prey nature. Two techniques of solution are presented and applied to two specific examples. The method of equivalent linearization is presented first, following which the concept of an additive corrective factor is introduced. Simulation shows that these solutions are accurate over a useful range of initial conditions and system parameters and are substantially better than those obtained by the classical principle of linearization. These literal parameter solutions permit insight into the overall system behavior while allowing easy assessment of the effects of variations of parameters without resorting to the traditional heuristic computer studies.