System identification: theory for the user
System identification: theory for the user
Automatica (Journal of IFAC)
Identification and control—closed-loop issues
Automatica (Journal of IFAC) - Special issue on trends in system identification
Robust and optimal control
Iterative Identification and Control: Advances in Theory and Applications
Iterative Identification and Control: Advances in Theory and Applications
SIAM Journal on Control and Optimization
Brief Robustness analysis tools for an uncertainty set obtained by prediction error identification
Automatica (Journal of IFAC)
Bias of indirect non-parametric transfer function estimates for plants in closed loop
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
System identification for achieving robust performance
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Various techniques of system identification exist that provide a nominal model and an uncertainty bound. An important question is what the implications are for the particular choice of the structure in which the uncertainty is described when dealing with robust stability/performance analysis of a given controller and when dealing with robust synthesis. It is shown that an amplitude-bounded (circular) uncertainty set can equivalently be described in terms of an additive, Youla parameter and @n-gap uncertainty. As a result, the choice of structure does not matter provided that the identification methods deliver optimal uncertainty sets rather than an uncertainty bound around a prefixed nominal model. Frequency-dependent closed-loop performance functions based on the uncertainty sets are again bounded by circles in the frequency domain, allowing for analytical expressions for worst-case performance and for the evaluation of the consequences of uncertainty for robust design. The results can be used to tune optimal experimental conditions in view of robust control design and in the further development of experiment-based robust control design methods.