Coupling Evolutionary Algorithms With Nonlinear Dynamical Systems: An Efficient Tool for Excitation Design and Optimization

Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or designing inputs used to drive a separate dynamical system of interest. By specifying an objective function and employing an evolutionary algorithm to manipulate the parameters governing the dynamics of the forcing system, the output of the driven system is made to approach an optimal response subject to desired constraints. The technique's versatility is demonstrated for two different applications: damage detection in structures and phase-locked loop disruption.

[1]  A. Nayfeh,et al.  Applied nonlinear dynamics : analytical, computational, and experimental methods , 1995 .

[2]  Alain Blanchard,et al.  Phase-Locked Loops: Application to Coherent Receiver Design , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Giorgio Biagetti,et al.  Nonlinear System Identification: An Effective Framework Based on the Karhunen–LoÈve Transform , 2009, IEEE Transactions on Signal Processing.

[4]  F. Takens Detecting strange attractors in turbulence , 1981 .

[5]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[6]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[7]  Keith Worden,et al.  Nonlinearity in Structural Dynamics , 2019 .

[8]  T. Carroll,et al.  Discontinuous and nondifferentiable functions and dimension increase induced by filtering chaotic data. , 1996, Chaos.

[9]  Thomas Schreiber,et al.  Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions , 1997, chao-dyn/9909044.

[10]  T L Carroll,et al.  Optimizing chaos-based signals for complex radar targets. , 2007, Chaos.

[11]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[12]  Michael D. Todd,et al.  A parametric investigation of state-space-based prediction error methods with stochastic excitation for structural health monitoring , 2007 .

[13]  Francis C. Moon Fractal Boundary for Chaos in a Two-State Mechanical Oscillator , 1984 .

[14]  Y. Wong,et al.  Differentiable Manifolds , 2009 .

[15]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[16]  J. Yorke,et al.  HOW MANY DELAY COORDINATES DO YOU NEED , 1993 .

[17]  O. Rössler An equation for continuous chaos , 1976 .

[18]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[19]  Paul V. Brennan,et al.  In-band disruption of a nonlinear circuit using optimal forcing functions , 2002 .

[20]  Phillip E. Pace,et al.  Detecting and Classifying Low Probability of Intercept Radar , 2009 .

[21]  Gang Hu,et al.  Turbulence control with local pacing and its implication in cardiac defibrillation. , 2007, Chaos.

[22]  J M Nichols,et al.  Use of chaotic excitation and attractor property analysis in structural health monitoring. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[24]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.