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In this thesis, we present a dynamical systems approach to adaptive controllers for locomotion control. The approach is based on a rigorous mathematical framework using nonlinear dynamical systems and is inspired by theories of self-organization. Nonlinear ...
Mixed Logical Dynamical (MLD) systems are introduced as a new system type. The MLD form is capable to model a broad class of systems arising in many applications: linear hybrid systems; sequential logical systems (finite state machines, automata); nonlinea ...
Zeros of continuous-time linear periodic systems are defined and their properties investigated. Under the assumption that the system has uniform relative degree, the zero-dynamics of the system is characterized and a closed-form expression of the blocking ...
Linear systems of equations with structured coefficient matrices arise in several applications in signal processing and communications. In this paper we develop fast algorithms for updating the solutions of such systems when the coefficient matrices underg ...
In this paper, the authors examine the use of chaotic systems based on the iteration of 1-D and 2-D piecewise linear chaotic maps in a coded modulation scheme. In the proposed scheme, the transmitted information is imposed on the dynamics of the chaotic sy ...
In this paper, the authors examine the use of chaotic systems based on the iteration of 1-D and 2-D piecewise linear chaotic maps in a coded modulation scheme. In the proposed scheme, the transmitted information is imposed on the dynamics of the chaotic sy ...
Predictive Control of Unstable Nonminimum-phase Systems K. Guemghar, B. Srinivasan, Ph. Mullhaupt, D. Bonvin ´ Institut d’Automatique, Ecole Polytechnique F´d´rale de Lausanne, ee CH-1015 Lausanne, Switzerland. Predictive control is a very effective approac ...
The extraction of information from measured data about the interactions taking place in a network of systems is a key topic in modern applied sciences. This topic has been traditionally addressed considering bivariate time series, providing methods that ar ...
Linear inverse problems with discrete data are equivalent to the estimation of the continuous-time input of a linear dynamical system from samples of its output. The solution obtained by means of regularization theory has the structure of a neural network ...
Abstract The problem of controlling nonlinear nonminimum-phase systems is considered, where standard input-output feedback linearization leads to unstable internal dynamics. This problem is handled here by using the observability normal form in conjunction ...