Commented University Calendar
Neural and Genetic Computing for Control of Dynamic Systems
Instructor:
Herbert Werner
Course Format:
2 hours Lecture
Period:
Summer Semester
Language:
English
Recommended Previous Knowledge:
Control Systems Theory and Design
Contents:
- Introduction to multilayer perceptron networks
- Nonlinear system identification using neural networks
- Neural network based predictive control
- Introduction to Genetic Algorithms (GA)
- Tuning PID controllers using GA
- Design of controllers with fixed structure using a hybrid Riccati-GA approach
- Introduction to relevant Matlab toolboxes (Neural Network Based System Identification , Neural Network Based Control System Design , Genetic Algorithm )
- Case studies and exercises in Matlab/Simulink
Learning Outcomes:
- knowledge: nonlinear system identification, predictive control, fixed-structure controller synthesis
- competence of methods: application of neural networks and evolutionary search in control engineering
- competence of systems: nonconvex optimization in control engineering
- social competence: communication in English
Reading Resources:
Werner, H., Lecture Notes „Neural Networks for Control of Dynamic Systems“, “Genetic Algorithms for Control”
L. Ljung "System Identification - Theory for the User" Prentice Hall, 1999
M. Norgaard, O. Ravn, N.K. Poulsen and L.K. Hansen "Neural Networks for Modelling and Control of Dynamic
Systems", Springer Verlag, London, 2003
M.T. Hagan, H.B. Demuth and M.H. Beale "Neural Network Design", Brooks Cole, 1995
Z. Michalewicz and D.B. Fogel, "How to Solve It: Modern Heuristics" (2nd Edition), Springer Verlag, Berlin
Performance Record:
oral exam
Workload:
90 hours total
Further Information:
Contact:
regelungstechnik(at)tuhh(dot)de
Credit points of this module can be found in the course plan for the corresponding course of study.
Last change: 24 Sep 2014