1,721,257 research outputs found
Trading robustness with optimality in nonlinear control via a post-optimization procedure
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Input-to-state stability for parameterized discrete-time time-varying nonlinear systems with applications
No full textInput-testate stability (ISS) of a parameterized familyof discrete-time time-varying nonlinear systems isinvestigated. A converse Lyapunov theorem for such systems is developed. We consider parameterized families of discrete-time systems and concentrate on a semiglobal practical property that naturally arises when an approximate discrete-time model is used to design a controller for a sampled-data system. Application of our main result to time-varying periodic systems is presented. This is then used to design a semiglobal practical ISS (SP-ISS) control law for the model of a wheeled mobile robot
A Hamilton-Jacobi setup for the static output feedback stabilization of nonlinear systems
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A Hamiltonian-Based Algorithm for Measurements Clustering
No full textThe paper describes a novel method for clustering points in the plane. The proposed algorithm is based on the notions of clustering function and level lines; the clusters are identified as the level sets corresponding to a reference value of the clustering function. The core idea is to regard the clustering function as a Hamiltonian function and to determine the level lines as the trajectories of the associated Hamiltonian system. The method is illustrated on two practical problems. © 2008 IEEE
Time-Dependent Hamiltonian Functions and the Representation of Dynamic Measurements Sets
No full textThe paper describes a method for representing the dynamic clustering of time-varying data. The main goal is the extension to the continuous-time dynamic scenario of an existing method for the static case. The clustering algorithm used in the static version of the method is based on the notion of clustering function and level lines; clusters are identified as the level sets corresponding to a reference value of the clustering function. The results presented herein refer to the case in which time is introduced as an input variable of the clustering function; thus the level sets are region of the three-dimensional space and level lines become level surfaces. © 2009 EUCA
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