Systems Identification for Embedded Drive Systems

Sommersemester 24

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.


  • 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

MS Teams Zugangscode



Dates by arrangement
Building E2.9, R. 2.12
Preliminary meeting: Thursday 18.04.24, 18:00h
Prof. habil. Dr.-Ing. Emanuele Grasso


Building E2.9 / Room 2.12
Dates by Arrangement
Niklas König, M.Sc.

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