Topic
Transfer function
About: Transfer function is a research topic. Over the lifetime, 14362 publications have been published within this topic receiving 214983 citations. The topic is also known as: system function & network function.
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TL;DR: The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box-Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms.
94 citations
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06 Jul 2007
TL;DR: In this paper, the phase-vs-distance model is directly evaluated during actual run-time operation of the TOF system, and two components are proposed: electrical modeling of phase and distance characteristics that depend upon electrical rather than geometric characteristics of the sensing system and elliptical modeling that depending upon geometric rather than electrical characteristics of sensing system.
Abstract: Rapid calibration of a TOF system uses a stationary target object and electrically introduces phase shift into the TOF system to emulate target object relocation. Relatively few parameters suffice to model a parameterized mathematical representation of the transfer function between measured phase and Z distance. The phase-vs-distance model is directly evaluated during actual run-time operation of the TOF system. Preferably modeling includes two components: electrical modeling of phase-vs-distance characteristics that depend upon electrical rather than geometric characteristics of the sensing system, and elliptical modeling that phase-vs-distance characteristics that depending upon geometric rather than electrical characteristics of the sensing system.
94 citations
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23 May 2003TL;DR: In this paper, the adaptive control of continuous-time linear dynamic systems preceded by an unknown dead-zone in state space form is discussed and a lemma to simplify the error equation between the plant and the matching reference model is introduced which allows the development of a robust adaptive control scheme by involving the deadzone inverse terms.
Abstract: The adaptive control of continuous-time linear dynamic systems preceded by an unknown dead-zone in state space form is discussed. A lemma to simplify the error equation between the plant and the matching reference model is introduced which allows the development of a robust adaptive control scheme by involving the dead-zone inverse terms. This adaptive control law ensures global stability of the entire system and achieves the desired tracking precision even when the slopes of the dead-zone are unequal. Simulations performed on a typical linear system illustrate and clarify the validity of this approach.
94 citations
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TL;DR: In this paper, the problem of least-squares estimation of closed-loop systems is examined and the conditions for uniqueness and consistency of the estimates of the forward-path transfer function are determined.
Abstract: The problem of least-squares estimation of closed-loop systems is examined. The particular configuration considered is the single input-single output discrete linear system controlled by a linear, stationary feedback regulator. Results are obtained which determine the conditions for uniqueness and consistency of the least-squares estimates of the forward-path transfer function. In particular, it is shown that if two orthogonal unobservable noise sources are present, one in each path, the system is uniquely identifiable. When the feedback path is noise-free the uniqueness of the estimates is dependent upon the order of the regulator regression polynomials. The consistency of the estimates in the case of a white forward-path disturbance is assured provided that there is at least one delay term in the loop.
94 citations
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TL;DR: A parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy that has good performance and the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced.
Abstract: This paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-...
94 citations