93,959 research outputs found
Model-Based Iterative Learning Control Applied to an Industrial Robot with Elasticity
In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency
A robust loop-shaping approach to fast and accurate nanopositioning
Peer reviewedPreprin
Nonlinear normal modes, modal interactions and isolated resonance curves
The objective of the present study is to explore the connection between the
nonlinear normal modes of an undamped and unforced nonlinear system and the
isolated resonance curves that may appear in the damped response of the forced
system. To this end, an energy balancing technique is used to predict the
amplitude of the harmonic forcing that is necessary to excite a specific
nonlinear normal mode. A cantilever beam with a nonlinear spring at its tip
serves to illustrate the developments. The practical implications of isolated
resonance curves are also discussed by computing the beam response to sine
sweep excitations of increasing amplitudes.Comment: Journal pape
Online identification of a two-mass system in frequency domain using a Kalman filter
Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies
Performance, robustness and sensitivity analysis of the nonlinear tuned vibration absorber
The nonlinear tuned vibration absorber (NLTVA) is a recently-developed
nonlinear absorber which generalizes Den Hartog's equal peak method to
nonlinear systems. If the purposeful introduction of nonlinearity can enhance
system performance, it can also give rise to adverse dynamical phenomena,
including detached resonance curves and quasiperiodic regimes of motion.
Through the combination of numerical continuation of periodic solutions,
bifurcation detection and tracking, and global analysis, the present study
identifies boundaries in the NLTVA parameter space delimiting safe, unsafe and
unacceptable operations. The sensitivity of these boundaries to uncertainty in
the NLTVA parameters is also investigated.Comment: Journal pape
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Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception.
Beat induction, the means by which humans listen to music and perceive a steady pulse, is achieved via a perceptualand cognitive process. Computationally modelling this phenomenon is an open problem, especially when processing expressive shaping of the music such as tempo change.To meet this challenge we propose Adaptive Frequency Neural Networks (AFNNs), an extension of Gradient Frequency Neural Networks (GFNNs).GFNNs are based on neurodynamic models and have been applied successfully to a range of difficult music perception problems including those with syncopated and polyrhythmic stimuli. AFNNs extend GFNNs by applying a Hebbian learning rule to the oscillator frequencies. Thus the frequencies in an AFNN adapt to the stimulus through an attraction to local areas of resonance, and allow for a great dimensionality reduction in the network.Where previous work with GFNNs has focused on frequency and amplitude responses, we also consider phase information as critical for pulse perception. Evaluating the time-based output, we find significantly improved re-sponses of AFNNs compared to GFNNs to stimuli with both steady and varying pulse frequencies. This leads us to believe that AFNNs could replace the linear filtering methods commonly used in beat tracking and tempo estimationsystems, and lead to more accurate methods
Detecting chaos in particle accelerators through the frequency map analysis method
The motion of beams in particle accelerators is dominated by a plethora of
non-linear effects which can enhance chaotic motion and limit their
performance. The application of advanced non-linear dynamics methods for
detecting and correcting these effects and thereby increasing the region of
beam stability plays an essential role during the accelerator design phase but
also their operation. After describing the nature of non-linear effects and
their impact on performance parameters of different particle accelerator
categories, the theory of non-linear particle motion is outlined. The recent
developments on the methods employed for the analysis of chaotic beam motion
are detailed. In particular, the ability of the frequency map analysis method
to detect chaotic motion and guide the correction of non-linear effects is
demonstrated in particle tracking simulations but also experimental data.Comment: Submitted for publication in Chaos, Focus Issue: Chaos Detection
Methods and Predictabilit
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