Optimal experiment designs with respect to the intended model application
Automatica (Journal of IFAC)
System identification: theory for the user
System identification: theory for the user
Rate of convergence of recursive estimators
SIAM Journal on Control and Optimization
Continuity properties of solutions to H2 and H∞ Riccati equations
Systems & Control Letters
For model-based control design, closed-loop identification gives better performance
Automatica (Journal of IFAC)
SIAM Journal on Control and Optimization
A Representation Theorem for the Error of Recursive Estimators
SIAM Journal on Control and Optimization
Robust optimal experiment design for system identification
Automatica (Journal of IFAC)
Brief Some results on optimal experiment design
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Adaptive signal processing for ARX system disturbed by complex noise
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Automatica (Journal of IFAC)
Input design as a tool to improve the convergence of PEM
Automatica (Journal of IFAC)
Hi-index | 22.15 |
A key problem in optimal input design is that the solution depends on system parameters to be identified. In this contribution we provide formal results for convergence and asymptotic optimality of an adaptive input design method based on the certainty equivalence principle, i.e. for each time step an optimal input design problem is solved exactly using the present parameter estimate and one sample of this input is applied to the system. The results apply to stable ARX systems with the input restricted to be generated by white noise filtered through a finite impulse response filter, or a binary signal obtained from the latter by a static nonlinearity.