Efficient Nonlinear Predictive Control Based on Structured Neural Models

Ławryńczuk, Maciej

doi:10.2478/v10006-009-0019-1

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Once the information about the average state-ofdamage of a collective is obtained, strategies for extending the average system usage, as proposed by Söffker and Rakowsky (1997), Ławryńczuk (2009), andŁawryńczuk andTatjewski (2010), become possible.…”

Section: Resultsmentioning

confidence: 99%

Adaptive modeling of reliability properties for control and supervision purposes

Dettmann

SéOuffker

2011

International Journal of Applied Mathematics and Computer Science

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Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damage level is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical) stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Several mathematical modeling approaches are known in the literature, focusing on the task of how loading effects damage progression (e.g., Wöhler, 1870) for wear processes. Those models are only valid for specific loading conditions, a priori assumptions, set points, etc. This contribution proposes a general model, covering adequately the deterioration of a set of comparable systems under comparable loading. The main goal of this contribution is to derive the loading-damage connection directly from observation without assuming any damage models at the outset. Moreover, the connection is not investigated in detail (e.g., to examine the changes in material, etc.) but only approximated with a probabilistic approach. The idea is subdivided into two phases: A problem-specific relation between loading applied (to a structure, which contributes to the stress) and failure is derived from simulation, where a probabilistic approach only assumes a distribution function. Subsequently, an adequate general model is set up to describe deterioration progression. The scheme will be shown through simulation-based results and can be used for estimation of the remaining useful life and predictive maintenance/control.

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Section: Resultsmentioning

confidence: 99%

Adaptive modeling of reliability properties for control and supervision purposes

Dettmann

SéOuffker

2011

International Journal of Applied Mathematics and Computer Science

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“…In particular, models of dynamic processes in the form of difference equations can be easily transformed to the above-mentioned state-space form. This case includes also neural-network models important for on-line applications of MPC control (Tatjewski and Ławryńczuk, 2006;Ławryńczuk, 2009;Ławryńczuk and Tatjewski, 2010).…”

Section: Mpc With a State-space Model And A Measured Statementioning

confidence: 99%

Disturbance modeling and state estimation for offset-free predictive control with state-space process models

Tatjewski

2014

International Journal of Applied Mathematics and Computer Science

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Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2×2 example process problem.

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“…For instance, multiple MISO neural network NARX models of the nonlinear MIMO processes have been employed for model predictive control in [21,22]. Moreover, a structured MIMO neural network model has been used instead of a NARX model for the model predictive control purposes in [23]. A detailed discussion on neural network model based predictive control can be found in [24].…”

Section: Introductionmentioning

confidence: 99%

A support vector machine based control application to the experimental three-tank system

İplikçi

2010

ISA Transactions

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Efficient Nonlinear Predictive Control Based on Structured Neural Models

Cited by 10 publications

References 22 publications

Adaptive modeling of reliability properties for control and supervision purposes

Adaptive modeling of reliability properties for control and supervision purposes

Disturbance modeling and state estimation for offset-free predictive control with state-space process models

A support vector machine based control application to the experimental three-tank system

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