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A framework for identification oriented robust controller design is developed. The model is identified from open-loop ilo-data and contains parametric uncertainties as well as an additive and norm-bounded error. The set of all robustly stabilising controllers, that additionally guarantee robust performance is characterised by a system of second order cones, which can be efficiently solved by interior...
A general framework for the design of multivariable control systems subject to hard constraints on each control channel is developed. The design procedure is an extension of the well-known H∞ Loop Shaping Design Procedure and is based on the calculation of the maximum possible control amplitude for a certain class of reference signals. Special attention is given to the adaption of the design weights...
The design of optimal controllers for systems subject to hard bounds on the control signal is considered. Optimality refers to the fact that we achieve the smallest possible worst case error (i.e. difference between reference signal and plant output) during runtime. The external reference signal is bounded in amplitude and rate, which encounters for many practical situations. Moreover, a conservative...
The design of robust controllers that guarantee prescribed hard bounds on the control signal when facing model uncertainty is considered. Our approach to avoid a conservative design is to describe the external signals quite accuratly by means of “admissible sets”, hard bounded in amplitude and rate. The core problem to solve is the calculation of the maximum output amplitude of a set of systems. This...
A framework for identification oriented robust controller design is developed. The model is identified from open-loop i/o-data and contains parametric uncertainties as well as an additive and norm-bounded error. The set of all robustly stabilising controllers, that additionally guarantee robust performance is characterised by a system of second order cones, which can be efficiently solved by interior...
Identification for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identification, that aim at dealing with contributions from the two main uncertainty sources: unmodeled dynamics and noise affecting the data. In particular, non-stationary Stochastic Embedding, Model Error...
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