Combination resonance in a multi-excited weakly nonlinear vibration absorber

It is established that oscillators with a weakly nonlinear spring, also resonates for excitations with a different frequency than its natural frequency. The studied type of resonance is called combination resonance, that occurs if the natural frequency approximately equals a sum of the excitation frequencies. This resonance can dissipate energy from several frequencies simultaneously. If a weakly nonlinear oscillator is used as an absorber on a main system, it can damps several vibration modes simultaneously. If combination resonance occurs in the absorber, it vibrates with the excited frequencies and its own natural frequency, which is higher as it is a sum of the excitation frequencies. This implies a potentially higher speed and dissipation. A tuning method is proposed to ensure this resonance in the weakly nonlinear absorbers. Additionally, a structural modification is added to the absorber, which increases the range of vibration energy of the main system that yields combination resonances. Simulations are performed that validate the theoretical analysis and tuning

Evolutionary-based sparse regression for the experimental identification of duffing oscillator

In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. The results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. The proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics

First order plus frequency dependent delay modeling : new perspective or mathematical curiosity?

The first-order-plus-dead-time model (FOPDT) is a popular simplified representation of higher order dynamics. However, a well known drawback is the rapid decrease of the frequency response accuracy with increasing process order. This especially applies to the higher frequency range. Literature offers solutions by extending this three parameter model with more parameters. Here, a fractional dead time is proposed. As such, a Frequency-Dependent Delay (FDD) is introduced, which offers a better approximation. As the fractional-order term introduces nonlinear coupling between the phase and the magnitude of the process, the fitting of the function becomes an iterative process, so a constrained multi-objective optimization is needed. This novel model, first-order-plus-frequency-dependent-delay or FOPFDD is fitted on a real electrical ladder network of resistors and capacitors of four and eight parts. The classic model, which is clearly a special case of the new model, is outperformed in the entire bandwidth

Sparse Identification of Nonlinear Duffing Oscillator From Measurement Data

In this paper we aim to apply an adaptation of the recently developed technique of sparse identification of nonlinear dynamical systems on a Duffing experimental setup with cubic feedback of the output. The Duffing oscillator described by nonlinear differential equation which demonstrates chaotic behavior and bifurcations, has received considerable attention in recent years as it arises in many real-world engineering applications. Therefore its identification is of interest for numerous practical problems. To adopt the existing identification method to this application, the optimization process which identifies the most important terms of the model has been modified. In addition, the impact of changing the amount of regularization parameter on the mean square error of the fit has been studied. Selection of the true model is done via balancing complexity and accuracy using Pareto front analysis. This study provides considerable insight into the employment of sparse identification method on the real-world setups and the results show that the developed algorithm is capable of finding the true nonlinear model of the considered application including a nonlinear friction term.Comment: 6 pages, 8 figures, conference pape