Systems Identification for Embedded Drive Systems

Wintersemester 2020/2021

Learning Target

This lecture is intended to provide the necessary mathematical tools needed for the identification of linear dynamic systems for supporting the design of real-time controllers with particular attention to motor drive applications. Moreover, an insight to parameter identification of dynamic systems will be provided. Finally, both multilayer perceptron and radial basis neural networks will be introduced together with the basis of genetic algorithms as support for system identification. Practical exercises based on real applications will be proposed along with the lecture time.

Content

  • Overview on Linear Time-Invariant Systems (LTIs)
  • AR(X)/ARMA(X) Systems
  • Normalized Least Mean-Squares and Recursive Least Mean-Squares Algorithms
  • Problem of Identification and Solutions for LTIs
  • Identification of Linear Systems Parameters
  • Applications of System Identification to Control of Electrical-Drives
  • Multilayer Perceptron and Radial Basis Neural Networks
  • Introduction to Genetic Algorithms
  • Implementation of System Identification Algorithms to Embedded Drive Systems

Lecture

Building E2.9 Room 2.12
First meeting: 21.10.21 14:00
Dr.-Ing. Emanuele Grasso

Tutorial

Building E2.9 / Room 2.12
Dates by Arrangement
Riccardo Mandriota, M.Sc.

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