Title :
Input Design in Worst-Case System Identification Using Binary Sensors
Author :
Casini, Marco ; Garulli, Andrea ; Vicino, Antonio
Author_Institution :
Dipt. di Ing. dell´´Inf., Univ. di Siena, Siena, Italy
fDate :
5/1/2011 12:00:00 AM
Abstract :
This technical note addresses system identification using binary-valued sensors in a worst-case set-membership setting. The main contribution is the solution of the optimal input design problem for identification of scalar gains, which is instrumental to the construction of suboptimal input signals for identification of FIR models of arbitrary order. Two different cost functions are considered for input design: the maximum parametric identification error and the relative uncertainty reduction with respect to the minimum achievable error. It is shown that in the latter case, the solution enjoys the property of being independent of the length of the identification experiment and as such it can be implemented as an optimal recursive procedure over a time interval of arbitrary length.
Keywords :
FIR filters; identification; FIR model; binary valued sensor; maximum parametric identification error; optimal input design problem; relative uncertainty reduction; scalar gains; suboptimal input signal construction; system identification; worst case set membership setting; Algorithm design and analysis; Cost function; Finite impulse response filter; Sensor systems; Stochastic processes; Uncertainty; Binary sensors; FIR models; input design; system identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2011.2107091