Volterra series

About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.

New techniques for vibration condition monitoring : Volterra kernel and Kolmogorov-Smirnov

Abstract: This research present a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smimov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smimov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.

A volterra series interpretation of some higher order conditions in optimal control

Abstract: A recent result giving the structure of the Volterra kernels in terms of Lie-brackets of vector fields allows a new formulation of some higher order necessary conditions of optimality. These conditions are equivalent to the generalized Legendre-Clebsch and Jacobson conditions.

A convex approach for NMPC based on second order Volterra series models

Abstract: Summary In model predictive control (MPC), the input sequence is computed, minimizing a usually quadratic cost function based on the predicted evolution of the system output In the case of nonlinear MPC (NMPC), the use of nonlinear prediction models frequently leads to non-convex optimization problems with several minimums This paper proposes a new NMPC strategy based on second order Volterra series models where the original performance index is approximated by quadratic functions, which represent a lower bound of the original performance index Convexity of the approximating quadratic cost functions can be achieved easily by a suitable choice of the weighting of the control increments in the performance index The approximating cost functions can be globally minimized by convex optimization techniques in order to compute the input sequence The minimization of the performance index is carried out by an iterative optimization procedure, which guarantees convergence to the solution Furthermore, for a nominal prediction model, asymptotic stability for the proposed NMPC strategy can be shown In the case of considering an estimation error in the prediction model, input-to-state practical stability is assured The control performance of the NMPC strategy is illustrated by experimental results Copyright © 2014 John Wiley & Sons, Ltd

Second-order statistical properties of nonlinearly distorted phase-shift keyed (PSK) signals

Abstract: In wireless communication systems operation of the amplifiers near saturation is often required for efficiency reasons, resulting in a nonlinearly distorted signal at the amplifier output. A popular model for the corresponding baseband equivalent nonlinear channel is a truncated Volterra series. By exploiting the bandpass nature of the channel and the statistical properties of phase-shift keyed signals, we show that the different terms in the Volterra series are white and uncorrelated with each other. This result is useful when considering blind equalization approaches for this class of systems.