Feedforward Control under the Presence of Uncertainty*

Faanes, Audun; Skogestad, Sigurd

doi:10.3166/ejc.10.30-46

Cited by 18 publications

(13 citation statements)

References 7 publications

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“…5. For PB(x), the intervals are [1,3], [2,5], [3,5], and [4,6], and for PB(y) the intervals are [0, 2], [1,3], [2,5], and [4,5]. To determine PB(x + y), each interval in PB(x) must be added to each interval in PB(y).…”

Section: P-boxesmentioning

confidence: 99%

“…Analysis of the impact of such uncertainties is clearly important in models of process dynamics, as used, for example, in state and parameter estimation 1, 2 and process control. [3][4][5] It is a challenging problem to propagate uncertainties through a nonlinear ODE system to rigorously predict the uncertainty in the model outputs. The problem is further complicated by the fact that the probability distributions describing the uncertainties may not be known precisely, if they are known at all.…”

Section: Introductionmentioning

confidence: 99%

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Probability bounds analysis for nonlinear dynamic process models

et al. 2011

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Dynamic process models frequently involve uncertain parameters and inputs. Propagating these uncertainties rigorously through a mathematical model to determine their effect on system states and outputs is a challenging problem. In this work, we describe a new approach, based on the use of Taylor model methods, for the rigorous propagation of uncertainties through nonlinear systems of ordinary differential equations (ODEs). We concentrate on uncertainties whose distribution is not known precisely, but can be bounded by a probability box (p-box), and show how to use p-boxes in the context of Taylor models. This allows us to obtain p-box representations of the uncertainties in the state variable outputs of a nonlinear ODE model. Examples having two to three uncertain parameters or initial states and focused on reaction process dynamics are used to demonstrate the potential of this approach. Using this method, rigorous probability bounds can be determined at a computational cost that is significantly less than that required by Monte Carlo analysis.

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Section: P-boxesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probability bounds analysis for nonlinear dynamic process models

et al. 2011

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“…Comparing the two kinds of controllers, feedback and feedforward, it has been demonstrated (Brosilow and Joseph 2002) that even when modelling errors are present in the system, a feedforward control can often reduce the effect of the disturbance better than the feedback control alone. Most of the studies on feedforward control deal with its application to industrial or engineering fields: Seborg et al (1989), Shinskey (1996), Marlin (2000), while a significant mathematical description can be found in Faanes and Skogestad (2004). Literature consistently reports that a feedforward controller is valuable when feedback control is not sufficient and that its practical use may improve the performance, but only when combined with a feedback controller.…”

Section: Introductionmentioning

confidence: 96%

The role of the feedforward paradigm in cognitive psychology

Basso

Belardinelli

2006

Cogn Process

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Feedforward control is a process adjusting behaviour in a continuative way. Feedforward takes place when an equilibrium state is disrupted and the system has to automatically retrieve the homeostatic stable state. It also occurs when a perturbation is previewed and must be eliminated in order to achieve a desired goal. According to the most general definition, a feedforward process operates by fixing the future representation of the desired state, the achieving of which stops the process. Then, feedforward works by means of the refinement determined by successive comparisons between the actual and target products. In its applications, a feedforward process is thought to be modulated by the subject's purpose and the environmental state. Over the years, the feedforward process has assumed different connotations in several contests of cognitive psychology. An overview of the research fields in psychology that significantly progressed with the introduction of a feedforward paradigm is provided by: (a) reviewing models in which the feedforward concept plays a fundamental role in the system control; (b) examining critical experiments related to the interaction of feedforward and feedback processes; (c) evidencing practical applications for some of the presented feedforward-based architectures.

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“…This is where , see (Faanes and Skogestad, 2002)). Note that with a larger model error, the positive effect of the feedforward controller may be reduced, and the feedforward action may even amplify the disturbances.…”

Section: Combined Local Pid and Feedforward Control (Lower Block Triamentioning

confidence: 99%

“…A more general analysis of feedforward control under the precence of uncertainty is given in (Faanes and Skogestad, 2002).…”

Section: Lower Block Triangular Controllermentioning

confidence: 99%

Controller design for serial processes

Faanes

Skogestad

2005

Journal of Process Control

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Conceptually, a multivariable controller uses the two basic principles of "Feedforward" action, based mainly on the model (for example the off-diagonal decoupling elements of the controllers), and feedback correction, based mainly on the measurements. The basic differences between feedback and feedforward control are well-known, and these differences also manifest themselves in the multivariable controller.Feedforward control may improve the performace significantly, but is sensitive to uncertainty, especially at low frequencies. Feedback control is very effective at lower frequencies where high feedback gains are allowed.In this paper we aim at obtaining insight into how a multivariable feedback controller works, with special attention to serial processes. Serial processes are important in the process industry, and the structure of this process makes it simple to classify the different elements of the multivariable controller.An example of neutralization of an acid in a series of three tanks is used to illustrate some of the ideas.

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Feedforward Control under the Presence of Uncertainty*

Cited by 18 publications

References 7 publications

Probability bounds analysis for nonlinear dynamic process models

Probability bounds analysis for nonlinear dynamic process models

The role of the feedforward paradigm in cognitive psychology

Controller design for serial processes

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