Abstract This is a demonstration of the PNLSS Toolbox 1.0. The toolbox designed to identify polynomial nonlinear state-space models from data. Nonlinear can describe wide range systems. An illustration provided on experimental data an electrical system mimicking forced Duffing oscillator, and numerical fluid dynamics problem.

The problem of chaos suppression by parametric perturbations is considered. Despite the widespread opinion that chaotic behavior may be stabilized by perturbations of any system parameter, we construct a counterexample showing that this is not necessarily the case. In general, chaos suppression means that parametric perturbations should be applied within a set of parameters at which the system ...

We investigate the behavior of a Duffing oscillator subjected to lateral periodic forcing. In our study, we analyze a mechanical model and compare our experimental data to computer simulations of the system. We have estimated the parameters of the model ODE so that the simulations provide a reasonable approximation to the physical system. We compare time series plots, Poincaré sections, and we ...

Two phenomena can produce chattering: switching of input control signal and the large amplitude of this switching (switching gain). To remove the switching of input control signal, dynamic sliding mode control (DSMC) is used. In DSMC switching is removed due to the integrator which is placed before the plant. However, in DSMC the augmented system (system plus the integrator) is one dimension bi...

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...

Studies of nonlinear dynamical systems with many degrees of freedom show that the behavior of these systems is significantly different as compared with the behavior of systems with less than two degrees of freedom. These findings motivated us to carry out a survey of research focusing on the behavior of high-dimensional chaos, which include onset of chaos, routes to chaos, and the persistence o...

We propose a new approach to the multiple–scale analysis of difference equations, in the context of the finite operator calculus. We derive the transformation formulae that map any given dynamical system, continuous or discrete, into a rescaled discrete system, by generalizing a classical result due to Jordan. Under suitable analytical hypotheses on the function space we consider, the rescaled ...

Identification and control problems for unknown chaotic dynamical systems are considered. Our aim is to regulate the unknown chaos to a fixed point or a stable periodic orbit. This is realized by following two contributions. First, a dynamic neural network is used as identifier. The weights of the neural networks are adjusted by the sliding mode technique. Second, we derive a local optimal cont...

We consider adaptive stabilization for a class of nonlinear second-order systems. Interpreting the system states as position and velocity, the system is assumed to have unknown, nonparametric position-dependent damping and stiffness coefficients. Lyapunov methods are used to prove global convergence of the adaptive controller. Furthermore, the controller is shown to be able reject constant dist...