Worst-case system identification in l/sub 1/: error bounds, optimal models and model reduction

Hakvoort, R.G.

doi:10.1109/cdc.1992.371684

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…For the purposes of implementing the control algorithms, input-output models describing the dynamics of the turbine-generator set were used, the parameters of which were updated at each simulation step using the recursive least squares method. The RLS algorithm enables the determination of an unknown model's parameters based on a set of input and output measurement data [31]. The RLS algorithm was chosen due to the simplicity of calculations, ease of implementation for the estimation of the parameters of the online model, and the possibility of taking into account the past input and output values of the object (as opposed to, e.g., the gradient method).…”

Section: Recursive Least Squares Schemementioning

confidence: 99%

“…The quadratic dynamic matrix control scheme and the corresponding structure of the generator have been presented in Figures 2 and 3, in the form on nonparametric models based on their step responses. The parameters of these models are estimated on the basis of a black-box approach, related to Formulae ( 9)- (11), identified in the online manner using the recursive least squares [31] scheme, to ensure coherency between the model, and the behavior of the system in different operating conditions, with appropriate input-output data measurements available. This approach is typically used when we are not interested in explaining the model structure, as presented simply by functional relations between input and output signals and put together with parameter models.…”

Section: Object Model Used Under Mpc/rls Frameworkmentioning

confidence: 99%

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Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

et al. 2023

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This paper discusses the issue of optimizing the communication between the components of a cooperating control system formed by a pair of MPC controllers of a nuclear power plant turbine set using online recursive least squares identification. It is proposed to use event-triggered communication, i.e., sending information only at selected time instants, as opposed to the standard approach where communication is triggered by time (time-triggered approach). The aim of this paper is to propose a change in the method of information exchange in the case of asynchronous communication between control system components and to prove its suitability for the selected application. Resignation from continuous communication in favor of sending information only at selected moments allows the load on the communication network to be reduced by approximately 90% while maintaining the quality of control.

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Section: Recursive Least Squares Schemementioning

confidence: 99%

Section: Object Model Used Under Mpc/rls Frameworkmentioning

confidence: 99%

Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

et al. 2023

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“…consider a simple form of impulse response truncation to minimize the l 1 norm of an error sequence (||e|| 1 = ∞ k=1 |e k |) [27]. The closest work to the problem considered here appears to be a result from the System Identification literature in which a reduced order model for a discrete-time system which minimizes the l 1 norm of an error metric is computed via a linear programming approach [8]. While there are substantial differences with the class of problems being considered here as compared to [8], the underlying technique of casting such problems as linear programs is the same.…”

Section: Mixed Moment Matching and Peak Error Objectivesmentioning

confidence: 99%

Solution methods for very highly integrated circuits.

Nong¹,

Thornquist²,

Chen³

et al. 2010

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While advances in manufacturing enable the fabrication of integrated circuits containing tens-to-hundreds of millions of devices, the time-sensitive modeling and simulation necessary to design these circuits poses a significant computational challenge. This is especially true for mixed-signal integrated circuits where detailed performance analyses are necessary for the individual analog/digital circuit components as well as the full system. When the integrated circuit has millions of devices, performing a full system simulation is practically infeasible using currently available Electrical Design Automation (EDA) tools. The principal reason for this is the time required for the nonlinear solver to compute the solutions of large linearized systems during the simulation of these circuits. The research presented in this report aims to address the computational difficulties introduced by these large linearized systems by using Model Order Reduction (MOR) to (i) generate specialized preconditioners that accelerate the computation of the linear system solution and (ii) reduce the overall dynamical system size. MOR techniques attempt to produce macromodels that capture the desired input-output behavior of larger dynamical systems and enable substantial speedups in simulation time. Several MOR techniques that have been developed under the LDRD on "Solution Methods for Very Highly Integrated Circuits" will be presented in this report. Among those presented are techniques for linear time-invariant dynamical systems that either extend current approaches or improve the time-domain performance of the reduced model using novel error bounds and a new approach for linear time-varying dynamical systems that guarantees dimension reduction, which has not been proven before. Progress on preconditioning power grid systems using multi-grid techniques will be presented as well as a framework for delivering MOR techniques to the user community using Trilinos and the Xyce circuit simulator, both prominent world-class software tools.

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“…Despite the fact that these procedures do not guarantee an bound, they are particularly efficient, easily computable, and close to the XFt, and Hankel norm optimal approximations. A limited procedure for 1, optimal model reduction is given in [7], at a much higher computational cost. The application of the standard model reduction procedure to the identified system may also be simplified by using the algorithm in [I] and [4], avoiding the computation of grammians.…”

Section: Parametric Delay Identificationmentioning

confidence: 99%

l/sub 1/ identification applied to a fluid dynamics problem

Parrilo

Pena

Galarza

1996

IEEE Trans. Contr. Syst. Technol.

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An application of robust identification techniques to a fluid dynamics problem is presented. The experimental data proceeds from a Taylor-Couette instability process. Its dynamics is usually modeled by a linear PDE (partial differential equation) which does not describe adequately certain oscillatory behavior. To this end we apply an identification technique to produce a more suitable description. The dynamics of the problem is excited by a tracer impulse and step injection. The output consists of the tracer concentration at the outlet of the experimental setup. The process presents a delay which is identified parametrically. For a given Reynolds number, the undelayed dynamics can be considered as linear and infinite dimensional. It is identified nonparametrically by means of a tuned asymptotically optimal !I robust identification procedure. Several experiments are performed on this fluid dynamics process for different Reynolds numbers and inputs. The identification procedure is applied to this experimental data to obtain linear delayed models in each case.

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Worst-case system identification in l/sub 1/: error bounds, optimal models and model reduction

Cited by 8 publications

References 10 publications

Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

Solution methods for very highly integrated circuits.

l/sub 1/ identification applied to a fluid dynamics problem

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