Input design using Markov chains for system identification

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. The state space is defined so that the Markov chain will generate a binary sequence. The probability distribution of the Markov chain will be 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 these models can improve system identification with respect to other input realization techniques.

• Type: Conference
• Year: 2009
• Where: CDC
• Authors: Chiara Brighenti, Bo Wahlberg, Cristian R. Rojas
• Keywords: CDC 2009, Control Systems, Input, Input Design Problem, Markov Chain