A History of Automatic Control

Bissell, Chris

doi:10.1007/978-3-540-78831-7_4

Cited by 32 publications

(21 citation statements)

References 30 publications

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“…The wide distribution of steam engines was initially prevented by the need to manually control the rotational speed of the shaft with a steam valve. The problem was solved by adopting a centrifugal governor (Figure ), invented by T. Mead, for automatically controlling the rotational speed in a steam engine . A reduction in the rotational speed was converted into an increase in the steam valve aperture.…”

Section: Introductionmentioning

confidence: 99%

Bang‐Bang Model for Regulation of Local Blood Flow

Golub

Pittman

2013

Microcirculation

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The classical model of metabolic regulation of blood flow in muscle tissue implies the maintenance of basal tone in arterioles of resting muscle and their dilation in response to exercise and/or tissue hypoxia via the evoked production of vasodilator metabolites by myocytes. A century-long effort to identify specific metabolites responsible for explaining active and reactive hyperemia has not been successful. Furthermore, the metabolic theory is not compatible with new knowledge on the role of physiological radicals (e.g., nitric oxide, NO, and superoxide anion, O2−) in the regulation of microvascular tone. We propose a model of regulation in which muscle contraction and active hyperemia are considered the physiologically normal state. We employ the “bang-bang” or “on/off” regulatory model which makes use of a threshold and hysteresis; a float valve to control the water level in a tank is a common example of this type of regulation. Active bang-bang regulation comes into effect when the supply of oxygen and glucose exceeds the demand, leading to activation of membrane NADPH oxidase, release of O2− into the interstitial space and subsequent neutralization of the interstitial NO. Switching arterioles on/off when local blood flow crosses the threshold is realized by a local cell circuit with the properties of a bang-bang controller, determined by its threshold, hysteresis and dead-band. This model provides a clear and unambiguous interpretation of the mechanism to balance tissue demand with a sufficient supply of nutrients and oxygen.

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Section: Introductionmentioning

confidence: 99%

Bang‐Bang Model for Regulation of Local Blood Flow

Golub

Pittman

2013

Microcirculation

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“…Since V is a standard Wiener process (to which W in (9) reduces in the case of g = 0, g 0 = I m ), the SDE (12) 5 generates an OU process [25] in R m , which is a stationary Gaussian diffusion process with zero mean and covariance function E(V T (t)V T (τ) T ) = e −Ω|t−τ| I m for all t, τ ∈ R, so that the variance E(|V T (t)| 2 ) = m remains constant in time, and T quantifies the correlation time of the process. 6 Since we will be concerned with the infinitehorizon asymptotic behaviour of stable systems, it will be convenient to endow ( 12)-( 14) with zero initial conditions:…”

Section: Filtered Signals and Transfer Functionsmentioning

confidence: 99%

“…To this end, we employ a parametric family of conformal maps (specified by an auxiliary time scale parameter and related to the Cayley transform [35]) between the unit disk and the right half-plane for the discrete and continuous time transfer functions. Up to a multiplicative constant, these maps are identical to Tustin's transform [6] for converting LCTI systems to linear discrete-time invariant (LDTI) systems. This results in a subsidiary LDTI system which lends itself to application of the anisotropy-based theory.…”

Section: Introductionmentioning

confidence: 99%

Anisotropy-based robust performance criteria for statistically uncertain linear continuous time invariant stochastic systems

Vladimirov

2019

Preprint

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This paper is concerned with robust performance criteria for linear continuous time invariant stochastic systems driven by statistically uncertain random processes. The uncertainty is understood as the deviation of imprecisely known probability distributions of the input disturbance from those of the standard Wiener process. Using a one-parameter family of conformal maps of the unit disk in the complex plane onto the right half-plane for discrete and continuous time transfer functions, the deviation from the nominal Gaussian white-noise model is quantified by the mean anisotropy for the input of a discrete-time counterpart of the original system. The parameter of this conformal correspondence specifies the time scale for filtered versions of the input and output of the system, in terms of which the worst-case root mean square gain is formulated subject to an upper constraint on the mean anisotropy. The resulting two-parameter counterpart of the anisotropy-constrained norm of the system for the continuous time case is amenable to state-space computation using the methods of the anisotropy-based theory of stochastic robust filtering and control, originated by the author in the mid 1990s.

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“…People have been trying to control and automate processes and different systems since ancient times [1]- [3]. Examples of this can be found in the ancient period and in the Middle Ages when such systems were based on mechanical control and automata.…”

Section: Introductionmentioning

confidence: 99%

Networked and Cloud Control Systems - Modern Challenges in Control Engineering

Popović

Naumović²

2019

IJEEC

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The paper describes development of some control techniques used in the feedback control systems. Modern control trends,with attention to networked control systems (NCS), and basic assumptions for introducing a new control concept – control in the cloudare described. Networked control systems techniques initiated the development of concept of Internet of Things and are fundamental incloud control systems design. Network induced delays that occur in NCSs, as a result of the presence of a communication network, andNCSs stability are considered, as well. Time delays and data dropouts influence on the NCS behavior and stability is analyzed, and somerelations for maximum allowable delay bound estimation are provided. It is shown, on example of DC motor control, that networkedsystem behaves in desired manner and remains stable if the delay is less then estimated maximum bound. When data dropouts occur,system delays become greater and lead to the system instability. Cloud control systems use some techniques of NCSs and cloudcomputing. These techniques are briefly presented and are necessary to complete control tasks in the cloud.

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A History of Automatic Control

Cited by 32 publications

References 30 publications

Bang‐Bang Model for Regulation of Local Blood Flow

Bang‐Bang Model for Regulation of Local Blood Flow

Anisotropy-based robust performance criteria for statistically uncertain linear continuous time invariant stochastic systems

Networked and Cloud Control Systems - Modern Challenges in Control Engineering

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