Brief paper: Frequency domain approach for designing sampling rates for system identification
Automatica (Journal of IFAC)
Paper: Optimal experiment design for linear systems with input-output constraints
Automatica (Journal of IFAC)
Brief paper: Synthesis of linear stochastic signals in identification problems
Automatica (Journal of IFAC)
Paper: Maximum-power validation of models without higher-order fitting
Automatica (Journal of IFAC)
On input signal synthesis in parameter identification
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
The paper considers the problem of input signal selection to maximize power of an Asymptotic Locally Most Powerful (ALMP) test. It is shown that the input signal which maximizes the test power simultaneously yields maximum accuracy of identification if disturbances are Gaussian. For linear multivariable discrete-time systems described by transfer functions both the input signal optimality criterion and its gradient are derived. This allows input signal optimization by means of a gradient hill-climbing method. The theory is illustrated by the optimal experiment design for a zero-order system with additive coloured disturbances.