Mastering System Identification in 100 Exercises
John Wiley & Sons, Apr 2, 2012 - Technology & Engineering - 282 pages
This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource.
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2 Generation and Analysis of Excitation Signals
3 FRF Measurements
4 Identification of Linear Dynamic Systems
5 Best Linear Approximation of Nonlinear Systems
6 Measuring the Best Linear Approximation of a Nonlinear System
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amplitude Amplitude dB amplitude spectrum best linear approximation bias BLA measurement black line Calculate cost function covariance matrix crest factor deﬁned detection lines DFT spectrum disturbing noise estimate excitation signals excited frequencies excited harmonics Exercise fast method feedback ﬁgure ﬁlter ﬁrst frequency band frequency domain Frequency Hz FRF measurements full multisine full-random multisine Gaussian noise GBLA gray line Hanning window impulse response in-band input and output input/output least squares linear approximation BLA linear dynamic system MATLAB model errors multisine BLA multisine excitation noise variance nonlinear system nonparametric noise model normally distributed Observations see Figure odd harmonics odd multisine odd nonlinear odd—random multisine parameters periodic excitation Plot power spectrum preﬁlter random harmonic grid random phase multisine realizations reference signal rms value robust method sample Section selected simulation sine spectra standard deviation static nonlinear stochastic nonlinear distortions system identiﬁcation tion toolbox transient weighted least squares