Input design using Markov chains for system identification
- Resource Type
- Conference
- Authors
- Brighenti, Chiara; Wahlberg, Bo; Rojas, Cristian R.
- Source
- Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. :1557-1562 Dec, 2009
- Subject
- Robotics and Control Systems
Power, Energy and Industry Applications
Computing and Processing
System identification
Frequency domain analysis
Cost function
Signal generators
Power system modeling
Covariance matrix
State-space methods
Probability distribution
Parameter estimation
White noise
- Language
- ISSN
- 0191-2216
This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the system. This allows to directly include input amplitude constraints in the input model by properly choosing the state space of the Markov chain, which is defined so that the Markov chain generates a multi-level sequence. The probability distribution of the Markov chain is shaped in order to minimize the cost function considered in the input design problem. Stochastic approximation is used to minimize that cost function. With this approach, the input signal to apply to the system can be easily generated by extracting samples from the optimal distribution. A numerical example shows how this method can improve estimation with respect to other input realization techniques.