This paper examines the design of input signals for identification of Hammerstein systems in a data-centric framework by addressing the optimal distribution of regressors. Data-centric estimation methods such as Model-on-Demand (MoD) generate local function approximations from a database of regressors at the current operating point. The data-centric input signal design formulation aims to develop sufficient support in the regressor space for the MoD estimator, while addressing time-domain constraints on the input and output signals. A numerical example is shown to highlight the benefit of proposed design over classical Pseudo Random Binary Sequence (PRBS), Multi Level Pseudo Random Sequence (MLPRS) and uniform random input designs.
|Title of host publication
|2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2013
|52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013 → Dec 13 2013
|Proceedings of the IEEE Conference on Decision and Control
|52nd IEEE Conference on Decision and Control, CDC 2013
|12/10/13 → 12/13/13
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization