Nonlinear System Identification Using Exponential Swept-Sine Signal

Novák, Antonín; Simon, Laurent; Kadlec, Frantisek; Lotton, Pierrick

doi:10.1109/tim.2009.2031836

Cited by 135 publications

(123 citation statements)

References 15 publications

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“…A Generalized Hammerstein model [16][17][18] represents a very good compromise between the complexity and the efficacy. It is made up of N parallel branches, with each branch consisting of a linear filter G n ( f ) preceded by an N-th order power static nonlinear function, for n = 1, N. The output u(t) of such a model to any input x(t) is governed by the following equation…”

Section: Nonlinear Modelsmentioning

confidence: 99%

“…Note that the definition of the exponential swept-sine (Equation (2)) does not contain the "−1" term contrary to the usual definition [16,38]. The original definition [38] led to a good estimation of amplitudes of Higher Harmonic Frequency Responses (HHFRs), but the phases of HHFRs have been estimated poorly.…”

Section: Synchronized Swept-sine Techniquementioning

confidence: 99%

“…The original definition [38] led to a good estimation of amplitudes of Higher Harmonic Frequency Responses (HHFRs), but the phases of HHFRs have been estimated poorly. A restriction of the parameters f 1 , f 2 , and T in order to synchronize the swept-sine signal has been proposed in [16], avoiding the problem of poor phase estimation and allowing an estimation of the filters G n ( f ) of the Generalized Hammerstein model. A similar approach has been proposed in [17].…”

Section: Synchronized Swept-sine Techniquementioning

confidence: 99%

“…The pickup under test (SSL-5 single-Coil pickup, Seymour Duncan, Santa Barbara, CA, USA) is set on a precision movement device, which allows for adjusting the distance at rest d 0 between the string and the magnet. The shaker is driven by a Synchronized Swept Sine signal [37] so that the nonlinearities of the whole system, that is, the shaker and the pickup in series can be easily identified using a Generalized Hammerstein model [16].…”

Section: Measurement Of the Pickup Nonlinearitiesmentioning

confidence: 99%

“…One of them, a Generalized Hammerstein model, has proved to be useful in modeling nonlinear systems with no physical knowledge of the system [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies †

et al. 2017

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Description of the physical behavior of electric guitars is still not very widespread in the scientific literature. In particular, the physical models describing a nonlinear behavior of pickups still requires some refinements. The study presented in this paper is focused on nonlinear modeling of the pickups. Two main issues are raised. First, is the currently most used nonlinear model (a Hammerstein model) sufficient for the complex nonlinear behavior of the pickup? In other words, would a more complex model, such as a Generalized Hammerstein that can deal better with the nonlinear memory, yield better results? The second troublesome issue is how to measure the nonlinear behavior of a pickup correctly. A specific experimental set-up allowing for driving the pickup in a controlled way (string displacement perpendicular to the pickup) and to separate the nonlinear model of the pickup from other nonlinearities in the measurement chain is proposed. Thanks to this experimental set-up, a Generalized Hammerstein model of the pickup is estimated for frequency range 15-500 Hz and the results are compared with a simple Hammerstein model. A comparison with experimental results shows that both models succeed in describing the pickup when used in realistic conditions.

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Section: Nonlinear Modelsmentioning

confidence: 99%

Section: Synchronized Swept-sine Techniquementioning

confidence: 99%

Section: Synchronized Swept-sine Techniquementioning

confidence: 99%

Section: Measurement Of the Pickup Nonlinearitiesmentioning

confidence: 99%

“…One of them, a Generalized Hammerstein model, has proved to be useful in modeling nonlinear systems with no physical knowledge of the system [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies †

et al. 2017

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Adaptive control and signal processing literature survey (No. 21)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. 1. ADAPTIVE SYSTEMS Adetola V, Guay M. Performance improvement in adaptive control of linearly parameterized nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(9):2182-2186. Bartoszewicz A, Nowacka-Leverton A. ITAE optimal sliding modes for third-order systems with input signal and state constraints. IEEE Transactions on Automatic Control 2010; 55(8):1928-1932. Bresch-Pietri D, Krstic M. Delay-adaptive predictor feedback for systems with unknown long actuator delay. IEEE Transactions on Automatic Control 2010; 55(9):2106-2112. Chen W, Jiao L, Li R, Li J. Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances. IEEE Transactions on Fuzzy Systems 2010; 18(4):674-685. Cheng C-C, Chien S-H, Shih F-C. Design of robust adaptive variable structure tracking controllers with application to rigid robot manipulators. Immersion and invariance adaptive control of nonlinearly parameterized nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(9):2209-2214. Miller D, Mansouri N. Model reference adaptive control using simultaneous probing, estimation, and control. IEEE Transactions on Automatic Control 2010; 55(9):2014-2029. Mizumoto I, Ohdaira S, Iwai Z. Output feedback strict passivity of discrete-time nonlinear systems and adaptive control system design with a PFC. Automatica 2010; 46(9):1503-1509. Moreno-Valenzuela J, Santibáñez V, Orozco-Manrııquez E, González-Hernández L. Theory and experiments of global adaptive output feedback tracking control of manipulators. IET Control Theory and Applications 2010; 4(9):1639-1654. Plestan F, Shtessel Y, Brégeault V, Poznyak A. New methodologies for adaptive sliding mode control. International Journal of Control 2010; 83(9):1907-1919.Reinhartz-Berger I, Soffer P, Sturm A. Extending the adaptability of reference models.

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Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

2021

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A state‐of‐the‐art review on various identification schemes proposed for the Hammerstein, Wiener, and Volterra systems is presented with respect to the special problems arising in the identification of unknown nonlinear systems. Past and recent developments in the field of nonlinear system identification, parameter estimation, and nonlinear control schemes along with the nonlinearity issues are also deeply investigated. A comprehensive analysis on various parameter estimation approaches and nonlinear control laws are made adding credit to the noteworthy contributions, constructive arguments, and remarkable breakthroughs of researchers in various research fields. The application of most popular nonlinear control strategies for many nonlinear systems in the existing literature are presented spotlighting their merits and major shortcomings. The challenges faced in the nonlinear control field and their emergences to future directions are projected.

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Nonlinear System Identification Using Exponential Swept-Sine Signal

Cited by 135 publications

References 15 publications

Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies †

Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies †

Adaptive control and signal processing literature survey (No. 21)

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

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