Search

A set membership method for right inversion of nonlinear systems from data is proposed in the paper. Both the cases where the system to invert is known or unknown and therefore identified from data are addressed. The method does not require the invertibility of the regression function describing the system and ensures tight bounds on the inversion error. In the case of unknown system, the method allows the derivation of a robust right-inverse, guaranteeing the inversion error bound for all the systems belonging to the uncertainty set which can be defined from the available prior and experimental information. Based on such a set membership inversion, two methods for robust control of nonlinear systems from data are introduced: nonlinear feed-forward control (NFFC) and nonlinear internal model control (NIMC). Both the design methods ensure robust stability and bounded tracking errors for all the systems belonging to the involved uncertainty set. Two applicative examples of robust control from data are presented: NFFC control of semi-active suspension systems and NIMC control of vehicle lateral dynamics. [ABSTRACT FROM AUTHOR]

A fast implementation of a given predictive controller for polynomial systems is introduced by approximating the optimal control law with a piecewise constant function defined over a hyper-cube partition of the system state space. Such a state-space partition is computed in order to guarantee stability, an a priori fixed trajectory error as well as input and state constraints fulfilment. The presented approximation procedure is achieved by solving a set of nonconvex polynomial optimization problems, whose approximate solutions are computed by means of semidefinite relaxation techniques for semialgebraic problems. [ABSTRACT FROM AUTHOR]

SUMMARY A new approach to design a Nonlinear Model Predictive Control law that employs an approximate model, derived directly from data, is introduced. The main advantage of using such models lies in the possibility to obtain a finite computable bound on the worst-case model error. Such a bound can be exploited to analyze the robust convergence of the system trajectories to a neighborhood of the origin. The effectiveness of the proposed approach, named Set Membership Predictive Control, is shown in a vehicle lateral stability control problem, through numerical simulations of harsh maneuvers. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Glycosaminoglycans (GAGs) from breast cyst fluid (BCF) of gross cysts, subdivided into apocrine and flattened, directly collected from 27 gross-cystic-breast-disease (GCBD)-affected women were analysed. Heparan sulfate, not further investigated, and chondroitin sulfate were identified. This last polysaccharide, in a content of 25-27 µg ml(-1) BCF and having a high molecular mass (~20 000-22 000), was found rich in glucuronic acid (~96%-98%) and mainly sulfated in position 4 of the N-acetyl-galactosamine (~60%-64%). Moreover, the presence of ~19%-24% of uncommon 4,6-O-disulfated disaccharides CS-E inside the polysaccharide chains with a high charge density of ~1.15-1.20 was determined. No substantial differences between apocrine and flattened cysts were observed. The current study describes the first effort to examine the yield and distribution of complex macromolecules like GAGs in BCF, and the understanding of their structure may help explain some functions associated with physiological and pathological conditions., (Copyright © 2013 John Wiley & Sons, Ltd.)