e-journal

Constrained Optimal Test Signal Design for Improved Prediction Error

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This paper presents a new efficient methodology for the optimal design of discrete test signals in black-box dynamic nonlinear system identification. The approach is based on a new criterion which weights the parameter covariances with the magnitudes of output sensitivities both to reduce the parameter estimation error and also allow the optimization of the output fitness. Optimization using this criterion has a low computational cost and in the case that the regressors are well chosen the performance index approximates that of the I-optimality criterion and results in high output fitness. The new method allows for the efficient use of numerical constrained global optimization algorithms to be applied to magnitude and rate constraints on system inputs and outputs, which are essential considerations in experimental applications. The approach should thus be employable as a component of
an iterative bootstrapping procedure for experimental system identification subject to safe operating limits. The approach is applied to the black-box nonlinear multiple-input multiple-output identification
of an automotive engine-fueling model as a benchmark. The results are compared with those obtained by other computationally efficient methods of both nonoptimal and optimal type. Statistical validation of the results shows that the design method using the new criterion gives test signals satisfying the required operational constraints which have superior outcomes in output prediction fit.
Note to Practitioners—New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static control models obtained from steady-state testing must be replaced with dynamical models.Althoughmany advances have been made in predictive models for internal combustion engines,
the engines involve so many complex nonlinear phenomena that black-boxmodelsmust be determined by experimental testing of final prototypes. Setting up automotive engine management control
systems for any new engine type consequently means that a costly experimental testing process must be undertaken. To obtain sufficiently accurate nonlinear dynamical models in this process, it is essential to properly excite all of themodes of behavior of the dynamics by an appropriately rich choice of test signal.Somephysical processes in the engine such as engine knock, however, can physically
damage the test engine and test equipment if the exciting signals are too aggressive, and so the signal and engine states must be constrained within strict limits. The use of transient models also
requires the recording of fast sampled time histories over long periods which significantly explodes the dimensionality of the test signal design problem. This paper addresses how the test signals
can be designed in a time efficient way using a new optimality apcation in automatic engine testing without exceeding the safe test signal limits. Many other complex industrial nonlinear dynamical
systems have similar experimental testing safety constraints and accuracy requirements and should be able to employ the new optimal test signal technique proposed in the paper.
Index Terms—Black-box modelling constraints, design of experiments (DoEs), nonlinear, prediction error, system identification.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 4, OCTOBER 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 4, OCTOBER 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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