136,284 research outputs found
Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit Models
In this paper we present the co-simulation of a PID class power converter
controller and an electrical circuit by means of the waveform relaxation
technique. The simulation of the controller model is characterized by a
fixed-time stepping scheme reflecting its digital implementation, whereas a
circuit simulation usually employs an adaptive time stepping scheme in order to
account for a wide range of time constants within the circuit model. In order
to maintain the characteristic of both models as well as to facilitate model
replacement, we treat them separately by means of input/output relations and
propose an application of a waveform relaxation algorithm. Furthermore, the
maximum and minimum number of iterations of the proposed algorithm are
mathematically analyzed. The concept of controller/circuit coupling is
illustrated by an example of the co-simulation of a PI power converter
controller and a model of the main dipole circuit of the Large Hadron Collider
Issues in the design of switched linear systems : a benchmark study
In this paper we present a tutorial overview of some of the issues that arise in the design of switched linear control systems. Particular emphasis is given to issues relating to stability and control system realisation. A benchmark regulation problem is then presented. This problem is most naturally solved by means of a switched control design. The challenge to the community is to design a control system that meets the required performance specifications and permits the application of rigorous analysis techniques. A simple design solution is presented and the limitations of currently available analysis techniques are illustrated with reference to this example
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
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