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Advanced autonomous vehicle strings rely on inter-vehicle communication in order to decrease the necessary safety gap so that fuel consumption can be decreased and road capacity can be increased. In case of failures of some communication channels, the corresponding back-up control strategy must be switched on. Maximal spacing errors of such back-up modes are analyzed and compared. Robustness to platoon...
The paper presents an iterative uncertainty modeling and robust controller synthesis method for linear parameter-varying (LPV) systems in linear fractional transformation (LFT) form. The goal of the method is to improve robust performance by exploiting modeling freedom in the structured uncertainty descriptions while ensuring model consistency with respect to measurement data. The proposed iterative...
The paper proposes a measure of robust performance based on frequency domain experimental data that allows non-conservative modeling of uncertainty. Given the nominal model of the plant and closed-loop performance specifications the iterative control design and remodeling of model uncertainty based on that measure leads to a controller with improved robust performance. The structured dynamic uncertainty...
Based on experimental data a joint method for nominal model error modeling and robust control is presented for linear time-invariant (LTI) systems. The goal of the method is to design a controller that attain robust quadratic performance and stability of the closed loop, and simultaneously construct an uncertainty model that is not invalidated by measurement data. The problem is motivated by the need...
A joint uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations in the general linear fractional transformation (LFT) form and additive disturbances - customary representation in modern robust control and 2) to select...
A joint uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations in the general linear fractional transformation (LFT) form and additive disturbances - customary representation in modern robust control and 2) to select...
Given a nominal model, an integrated uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations and disturbances in the general linear fractional transformation (LFT) form - customary representation in modern robust...
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