Study on Nonlinear Dynamics and Chaos Suppression of Active Magnetic Bearing Systems Based on Synchronization

Chang, Shun-Chang

doi:10.1155/2021/5549775

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…From the conclusions of the previous section, it can be seen that since the coupling between the radial and axial control has been neglected, the simulation can be performed without considering the rotor Z-direction degrees of freedom. Additionally, because this closed-loop system is second-order unstable, PID control must be combined with it to reach a steady state [13] .…”

Section: Simulink Simulation Of Radial 4-dof Systemmentioning

confidence: 99%

Radial 4-DOF decoupling control of active magnetic bearing

Bai,

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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This paper mainly focuses on the coupling effect and decoupling strategy of the active magnetic bearing (AMB). First, the AMB’s operating mechanism is examined, and a linearized model of the electromagnet-rotor is built. Second, the state equation for the five-degree-of-freedom (5-DOF) electromagnet-rotor is obtained using this linearized model. Combined with PID control, a radial magnetic bearing control model was built using MATLABSimulink to observe its control effect. Finally, to solve the coupling problem, a feedforward-feedback decoupling approach is suggested. Via simulation, it has been demonstrated that this approach can lessen the coupling effect and increase the static stability of the system.

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Section: Simulink Simulation Of Radial 4-dof Systemmentioning

confidence: 99%

Radial 4-DOF decoupling control of active magnetic bearing

Bai,

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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“…At present, nonlinear dynamics method [ 24 , 25 ] has been widely used in brain science research. For the study of EEG signals, the nonlinear dynamic methods mainly include correlation dimension, Lyapunov exponent, Kolmogonov entropy, approximate entropy, and sample entropy.…”

Section: Introduction Of Multivariate Transfer Entropy Algorithm and ...mentioning

confidence: 99%

Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy

Zhang

Xie

Fan

et al. 2022

Computational Intelligence and Neuroscience

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The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region.

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A New Robust Method to Investigate Dynamic Instability of FTV for the Double Tripod Industrial Driveshafts in the Principal Parametric Resonance Region

Bugaru

Vasile

2022

Applied Sciences

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The present work aims to design a robust method to detect and certify the deterministic chaos or ergodic process for the forced torsional vibrations (FTV) of a double tripod industrial driveshaft (DTID) in transition through the principal parametric resonance region (PPRR) which is considered by the researchers in the field as one of the most important resonance regions for the systems having parametric excitations. The DTID’s model for FTV considers the following effects: nonuniformities of inertial characteristics of the DTID’s elements, the harmonic torque excitation induced by the asynchronous electrical motor used for a heavy-duty grain mill, and the harmonic reaction torque generated by different granulation of the substance needed to be milled. Based on these aspects, a model of the FTV for the DTID was designed which was a modified, physically consistent model already used by the authors to investigate the FTV of automotive driveshafts (homokinetic transmission). For the DTID elements, the dynamic instability for nonstationary FTV in the PPRR using time–history analysis (THA) was analyzed—THA represents the phase portraits. Time–history analysis is a detection method for possible chaotic dynamic behavior for the nonstationary FTV (NFTV) in transition through PPRR. If this dynamic behavior was seen, a new robust method LEA–PM was created to certify and confirm the deterministic chaos for the NFTV of DTID. The new method, LEA–PM, is composed of the Lyapunov exponent’s approach (LEA) coupled with the Poincaré Map (PM) applied to the global system of differential equations that describe the FTV of DTID in the PPRR. This new robust method, which embeds LEA and PM, LEA–PM, establishes if the mechanical system has a deterministic chaotic dynamic behavior (strange attractor) or an ergodic dynamic process in this resonant region. LEA represents a new method that includes not only the maximal Lyapunov exponent method (MLEM) but also new mathematical criteria that is “the sum of all Lyapunov exponents has to be negative” which, coupled with MLEM, indicates the presence of deterministic chaos (strange attractors). THA–LEA–PM had been used for the NFTV of DTID computing the phase portraits, the Lyapunov exponents, and representing the Poincaré Maps of the NFTV for the DTID’s elements in transition through PPRR, founding deterministic chaos or ergodic dynamic behavior. Based on the obtained results, numerical simulations revealed the pitting manifestations of the DTID’s elements, typical for the geared systems transmission, mentioned recently in experimental data research for the homokinetic transmissions. Using the new robust method, THA–LEA–PM (time–history analysis coupled with LEA–PM) can be used in future research for chaotic dynamic analysis of DTID’s NFTV transition through superharmonic resonances, subharmonic resonances, combination resonances, and internal resonances. Time–history analysis as a detection method for chaos and LEA–PM as a certifying method for deterministic chaos can be integrated as a design tool for DTID’s FTV control of the homokinetic transmission.

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Study on Nonlinear Dynamics and Chaos Suppression of Active Magnetic Bearing Systems Based on Synchronization

Cited by 3 publications

References 22 publications

Radial 4-DOF decoupling control of active magnetic bearing

Radial 4-DOF decoupling control of active magnetic bearing

Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy

A New Robust Method to Investigate Dynamic Instability of FTV for the Double Tripod Industrial Driveshafts in the Principal Parametric Resonance Region

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